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PDF Output of CLIC (clustering by inferred co-expression) Dataset: Num of genes in input gene set: 19 Total number of genes: 16493 CLIC PDF output has three sections: 1) Overview of Co-Expression Modules (CEMs) Heatmap shows pairwise correlations between all genes in the input query gene set. Red lines shows the partition of input genes into CEMs, ordered by CEM strength. Each row shows one gene, and the brightness of squares indicates its correlations with other genes. Gene symbols are shown at left side and on the top of the heatmap. 2) Details of each CEM and its expansion CEM+ Top panel shows the posterior selection probability (dataset weights) for top GEO series datasets. Bottom panel shows the CEM genes (blue rows) as well as expanded CEM+ genes (green rows). Each column is one GEO series dataset, sorted by their posterior probability of being selected. The brightness of squares indicates the gene's correlations with CEM genes in the corresponding dataset. CEM+ includes genes that co-express with CEM genes in high-weight datasets, measured by LLR score. 3) Details of each GEO series dataset and its expression profile: Top panel shows the detailed information (e.g. title, summary) for the GEO series dataset. Bottom panel shows the background distribution and the expression profile for CEM genes in this dataset. Overview of Co-Expression Modules (CEMs) with Dataset Weighting Scale of average Pearson correlations Num of Genes in Query Geneset: 19. Num of CEMs: 1. 0.0 0.2 0.4 0.6 0.8 1.0 Mrpl42 Mrps28 Mrps17 Mrps16 Mrps35 Mrps33 Mrps22 Mrps18a Mrps18c Mrps36 Mrps24 Mrps21 Mrps26 Mrps31 Mrps18b Mrps9 Mrps25 Mrps15 Mrps11 Mrpl42 Mrps28 Mrps17 Mrps16 Mrps35 Mrps33 Mrps22 Mrps18a CEM 1 (457 datasets) Mrps18c Mrps36 Mrps24 Mrps21 Mrps26 Mrps31 Mrps18b Mrps9 Mrps25 Mrps15 Singletons Mrps11 Symbol Num ofCEMGenes:16.Predicted670.SelectedDatasets:457.Strength:28.1 CEM 1,Geneset"[G]mitochondrialsmallribosomalsubunit",Page1 Mrps18b Mrps18c Mrps18a Ndufb10 Timm8b Timm13 Chchd1 Mrps10 Mrps31 Mrps26 Mrps21 Mrps24 Mrps36 Mrps22 Mrps33 Mrps35 Mrps16 Mrps17 Mrps28 Ndufb5 Ndufb3 Ndufb7 Ndufb6 Uqcr11 Ndufa8 Ndufc1 Ndufa5 Ndufs8 Ndufv2 Mrpl28 Mrpl40 Mrpl47 Mrpl13 Mrpl55 Mrpl18 Mrpl46 Mrpl11 Mrpl22 Mrpl12 Mrpl42 Grpel1 Atp5j2 Dpy30 Naa38 Mrps7 Mrps9 Polr2j Mrpl2 Slirp Uxt 0.0 1.0 GSE8044 [6] GSE16874 [12] GSE14004 [9] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE46496 [9] GSE51080 [18] GSE21944 [6] GSE20954 [14] GSE48397 [10] GSE20152 [8] GSE46606 [30] GSE45619 [6] GSE41759 [14] GSE20391 [11] GSE18907 [12] GSE18587 [9] GSE31940 [8] GSE42299 [8] GSE11973 [6] GSE59437 [30] GSE47872 [6] GSE38754 [40] GSE12518 [6] GSE11572 [12] GSE38031 [8] GSE36665 [6] GSE6674 [15] GSE13071 [15] GSE51608 [6] GSE15729 [15] GSE18135 [18] GSE33101 [8] GSE6875 [8] GSE11333 [6] GSE15587 [6] GSE15541 [12] GSE23833 [12] GSE13611 [8] GSE10912 [6] GSE46090 [12] GSE41095 [6] GSE46723 [6] GSE31004 [8] GSE21299 [12] GSE43373 [130] GSE11220 [44] GSE17497 [10] GSE5035 [12] GSE53951 [10] GSE52597 [7] GSE13693 [9] GSE15155 [12] GSE32277 [33] GSE13302 [30] GSE52474 [154] GSE14769 [24] GSE27811 [9] GSE4535 [6] GSE9316 [12] GSE13432 [12] GSE21063 [24] GSE43042 [6] GSE15624 [12] GSE26616 [6] GSE6837 [8] GSE59202 [8] GSE40282 [6] GSE5371 [8] GSE26830 [12] GSE38304 [8] GSE11222 [42] GSE6623 [12] GSE46091 [8] GSE25766 [6] GSE9652 [11] GSE30083 [12] GSE41907 [7] GSE6030 [6] GSE48811 [20] GSE10273 [9] GSE26671 [12] GSE17728 [12] GSE19954 [8] GSE13692 [8] GSE28389 [20] GSE30160 [6] GSE37907 [24] GSE13235 [9] GSE25778 [6] GSE9975 [36] GSE8407 [17] GSE23408 [39] GSE23040 [6] GSE55356 [6] GSE44923 [16] GSE54056 [12] GSE20500 [6] GSE18396 [6] GSE46094 [10] GSE11818 [6] GSE10589 [6] GSE55607 [18] GSE39897 [36] GSE20987 [12] GSE42008 [6] GSE33121 [10] GSE15610 [12] GSE31244 [6] GSE3313 [24] GSE32311 [11] GSE42473 [15] GSE22824 [24] GSE11759 [6] GSE3530 [36] GSE16002 [9] GSE48884 [12] GSE52542 [9] GSE17322 [6] GSE20235 [6] GSE7020 [8] GSE14906 [6] GSE44175 [18] GSE7050 [18] GSE27816 [14] GSE12464 [23] GSE10210 [16] GSE35226 [12] GSE13635 [6] GSE21670 [16] GSE6867 [6] GSE53077 [8] GSE25252 [10] GSE46970 [15] GSE20523 [17] GSE46797 [6] GSE11291 [60] GSE19528 [8] GSE8836 [56] GSE42688 [8] GSE27092 [6] GSE23101 [20] GSE36826 [12] GSE47414 [18] GSE19338 [24] GSE46211 [18] GSE5891 [6] GSE7897 [60] GSE12430 [21] GSE12948 [9] GSE31313 [22] GSE12465 [14] GSE4712 [21] GSE8715 [6] GSE48790 [8] GSE20302 [12] CEM+ CEM GSE27379 [6] GSE10176 [6] GSE29082 [6] GSE31208 [8] GSE51385 [8] GSE21549 [6] 0.0 GSE27114 [6] GSE23600 [10] GSE31570 [6] Scale ofaveragePearsoncorrelations GSE17266 [59] GSE24210 [16] GSE28093 [6] GSE7275 [8] GSE33199 [64] GSE21041 [6] 0.2 GSE9717 [6] GSE32034 [14] GSE10627 [51] GSE17794 [44] GSE25645 [17] GSE13044 [59] GSE25295 [25] GSE13753 [10] GSE10849 [6] 0.4 GSE12908 [10] GSE56345 [9] GSE27848 [16] GSE7309 [12] GSE15330 [27] GSE3067 [28] GSE13493 [6] GSE10285 [8] GSE26096 [10] 0.6 GSE15293 [37] GSE15808 [29] GSE7705 [10] GSE18460 [16] GSE5041 [8] GSE28417 [12] GSE7460 [52] GSE58484 [15] GSE15741 [6] 0.8 GSE47777 [8] GSE14088 [9] GSE18704 [9] GSE25244 [9] Score 384.78 384.88 385.53 385.87 386.23 387.57 389.72 390.23 391.45 392.85 395.30 398.96 399.28 399.41 400.17 401.89 402.37 404.32 406.48 408.24 408.39 414.95 416.28 417.26 417.47 419.45 419.54 428.15 429.08 435.43 447.19 451.80 458.65 482.98 1.0 Notes 1110001J03Rik Gadd45gip1 Symbol Num ofCEMGenes:16.Predicted670.SelectedDatasets:457.Strength:28.1 CEM 1,Geneset"[G]mitochondrialsmallribosomalsubunit",Page2 BC003965 Tmem147 Timm17a Aurkaip1 Dnajc15 Timm10 Timm23 Ndufaf6 Cox7a2 Ccdc58 Mrps34 Mrps12 Sssca1 Ndufb9 Malsu1 Dctpp1 Ndufs3 Atp5g1 Ndufs7 Stoml2 Psmg1 Psmb6 Psmb3 Mrpl39 Mrpl48 Mrpl16 Mrpl34 Mrpl27 Mrpl41 Mrpl36 Gtf2h5 Atp5f1 C1qbp Hspe1 Uqcc2 Naa10 Mrp63 Polr2f Atp5h Polr2i Atp5k Cisd1 Bola3 Coq7 Cyc1 Clpp Fh1 Fxn 0.0 1.0 GSE8044 [6] GSE16874 [12] GSE14004 [9] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE46496 [9] GSE51080 [18] GSE21944 [6] GSE20954 [14] GSE48397 [10] GSE20152 [8] GSE46606 [30] GSE45619 [6] GSE41759 [14] GSE20391 [11] GSE18907 [12] GSE18587 [9] GSE31940 [8] GSE42299 [8] GSE11973 [6] GSE59437 [30] GSE47872 [6] GSE38754 [40] GSE12518 [6] GSE11572 [12] GSE38031 [8] GSE36665 [6] GSE6674 [15] GSE13071 [15] GSE51608 [6] GSE15729 [15] GSE18135 [18] GSE33101 [8] GSE6875 [8] GSE11333 [6] GSE15587 [6] GSE15541 [12] GSE23833 [12] GSE13611 [8] GSE10912 [6] GSE46090 [12] GSE41095 [6] GSE46723 [6] GSE31004 [8] GSE21299 [12] GSE43373 [130] GSE11220 [44] GSE17497 [10] GSE5035 [12] GSE53951 [10] GSE52597 [7] GSE13693 [9] GSE15155 [12] GSE32277 [33] GSE13302 [30] GSE52474 [154] GSE14769 [24] GSE27811 [9] GSE4535 [6] GSE9316 [12] GSE13432 [12] GSE21063 [24] GSE43042 [6] GSE15624 [12] GSE26616 [6] GSE6837 [8] GSE59202 [8] GSE40282 [6] GSE5371 [8] GSE26830 [12] GSE38304 [8] GSE11222 [42] GSE6623 [12] GSE46091 [8] GSE25766 [6] GSE9652 [11] GSE30083 [12] GSE41907 [7] GSE6030 [6] GSE48811 [20] GSE10273 [9] GSE26671 [12] GSE17728 [12] GSE19954 [8] GSE13692 [8] GSE28389 [20] GSE30160 [6] GSE37907 [24] GSE13235 [9] GSE25778 [6] GSE9975 [36] GSE8407 [17] GSE23408 [39] GSE23040 [6] GSE55356 [6] GSE44923 [16] GSE54056 [12] GSE20500 [6] GSE18396 [6] GSE46094 [10] GSE11818 [6] GSE10589 [6] GSE55607 [18] GSE39897 [36] GSE20987 [12] GSE42008 [6] GSE33121 [10] GSE15610 [12] GSE31244 [6] GSE3313 [24] GSE32311 [11] GSE42473 [15] GSE22824 [24] GSE11759 [6] GSE3530 [36] GSE16002 [9] GSE48884 [12] GSE52542 [9] GSE17322 [6] GSE20235 [6] GSE7020 [8] GSE14906 [6] GSE44175 [18] GSE7050 [18] GSE27816 [14] GSE12464 [23] GSE10210 [16] GSE35226 [12] GSE13635 [6] GSE21670 [16] GSE6867 [6] GSE53077 [8] GSE25252 [10] GSE46970 [15] GSE20523 [17] GSE46797 [6] GSE11291 [60] GSE19528 [8] GSE8836 [56] GSE42688 [8] GSE27092 [6] GSE23101 [20] GSE36826 [12] GSE47414 [18] GSE19338 [24] GSE46211 [18] GSE5891 [6] GSE7897 [60] GSE12430 [21] GSE12948 [9] GSE31313 [22] GSE12465 [14] GSE4712 [21] GSE8715 [6] GSE48790 [8] GSE20302 [12] CEM+ CEM GSE27379 [6] GSE10176 [6] GSE29082 [6] GSE31208 [8] GSE51385 [8] GSE21549 [6] 0.0 GSE27114 [6] GSE23600 [10] GSE31570 [6] Scale ofaveragePearsoncorrelations GSE17266 [59] GSE24210 [16] GSE28093 [6] GSE7275 [8] GSE33199 [64] GSE21041 [6] 0.2 GSE9717 [6] GSE32034 [14] GSE10627 [51] GSE17794 [44] GSE25645 [17] GSE13044 [59] GSE25295 [25] GSE13753 [10] GSE10849 [6] 0.4 GSE12908 [10] GSE56345 [9] GSE27848 [16] GSE7309 [12] GSE15330 [27] GSE3067 [28] GSE13493 [6] GSE10285 [8] GSE26096 [10] 0.6 GSE15293 [37] GSE15808 [29] GSE7705 [10] GSE18460 [16] GSE5041 [8] GSE28417 [12] GSE7460 [52] GSE58484 [15] GSE15741 [6] 0.8 GSE47777 [8] GSE14088 [9] GSE18704 [9] GSE25244 [9] Score 309.45 311.00 311.75 312.02 312.23 313.95 317.90 318.81 319.08 321.71 324.88 325.87 326.90 327.06 328.14 328.52 328.66 329.28 330.25 332.21 336.94 340.51 342.51 343.00 344.66 344.84 346.31 347.52 347.58 347.69 349.90 350.27 353.90 354.22 355.06 355.16 355.36 356.68 357.87 358.31 361.48 361.80 363.03 363.08 363.85 372.35 374.88 377.82 382.13 382.59 1.0 Notes 9430016H08Rik 1700021F05Rik 2810428I15Rik Symbol Num ofCEMGenes:16.Predicted670.SelectedDatasets:457.Strength:28.1 CEM 1,Geneset"[G]mitochondrialsmallribosomalsubunit",Page3 Tmem126a Fam195a Commd1 Samm50 Tomm22 Ndufaf5 Ndufaf2 Cox6b1 N6amt2 Tomm7 Exosc4 H2-Ke2 Apopt1 Ndufa4 Atp5g2 Ndufa2 Ndufa9 Gm561 Atp5c1 Cmss1 Suclg1 Psmb5 Mrpl23 Mrpl54 Mrpl43 Mrpl44 Mrpl32 Mrpl20 Cops6 Nop10 Cox5a Nudt2 Park7 Emg1 Emc6 Lsm4 Glrx3 Pyurf Apoo Nhp2 Atp5j Sdhd Atp5l Hint1 Deb1 Phb2 Ppa2 0.0 1.0 GSE8044 [6] GSE16874 [12] GSE14004 [9] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE46496 [9]
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    Cardiac SARS‐CoV‐2 infection is associated with distinct tran‐ scriptomic changes within the heart Diana Lindner, PhD*1,2, Hanna Bräuninger, MS*1,2, Bastian Stoffers, MS1,2, Antonia Fitzek, MD3, Kira Meißner3, Ganna Aleshcheva, PhD4, Michaela Schweizer, PhD5, Jessica Weimann, MS1, Björn Rotter, PhD9, Svenja Warnke, BSc1, Carolin Edler, MD3, Fabian Braun, MD8, Kevin Roedl, MD10, Katharina Scher‐ schel, PhD1,12,13, Felicitas Escher, MD4,6,7, Stefan Kluge, MD10, Tobias B. Huber, MD8, Benjamin Ondruschka, MD3, Heinz‐Peter‐Schultheiss, MD4, Paulus Kirchhof, MD1,2,11, Stefan Blankenberg, MD1,2, Klaus Püschel, MD3, Dirk Westermann, MD1,2 1 Department of Cardiology, University Heart and Vascular Center Hamburg, Germany. 2 DZHK (German Center for Cardiovascular Research), partner site Hamburg/Kiel/Lübeck. 3 Institute of Legal Medicine, University Medical Center Hamburg‐Eppendorf, Germany. 4 Institute for Cardiac Diagnostics and Therapy, Berlin, Germany. 5 Department of Electron Microscopy, Center for Molecular Neurobiology, University Medical Center Hamburg‐Eppendorf, Germany. 6 Department of Cardiology, Charité‐Universitaetsmedizin, Berlin, Germany. 7 DZHK (German Centre for Cardiovascular Research), partner site Berlin, Germany. 8 III. Department of Medicine, University Medical Center Hamburg‐Eppendorf, Germany. 9 GenXPro GmbH, Frankfurter Innovationszentrum, Biotechnologie (FIZ), Frankfurt am Main, Germany. 10 Department of Intensive Care Medicine, University Medical Center Hamburg‐Eppendorf, Germany. 11 Institute of Cardiovascular Sciences,
  • Using Edsurvey to Analyze NCES Data: an Illustration of Analyzing NAEP Primer

    Using Edsurvey to Analyze NCES Data: an Illustration of Analyzing NAEP Primer

    Using EdSurvey to Analyze NCES Data: An Illustration of Analyzing NAEP Primer Developed by Michael Lee, Paul Bailey, Ahmad Emad, Ting Zhang, Trang Nguyen, and Jiao Yu*† February 21, 2020 Overview of the EdSurvey Package National Assessment of Educational Progress (NAEP) datasets from the National Center for Education Statistics (NCES) require special statistical methods to analyze. Because of their scope and complexity, the EdSurvey package gives users functions to perform analyses that account for both complex sample survey designs and the use of plausible values. The EdSurvey package also seamlessly takes advantage of the LaF package to read in data only when required for an analysis. Users with computers that have insuÿcient memory to read in entire NAEP datasets can still do analyses without having to write special code to read in just the appropriate variables. This situation is addressed directly in the EdSurvey package—behind the scenes and without any special tuning by the user. Vignette Outline This vignette will describe the basics of using the EdSurvey package for analyzing NAEP data as follows. • Notes – Additional resources – Vignette notation – Software requirements • Setting up the environment for analyzing NCES data – Installing and loading EdSurvey – Philosophy of Conducting Analyses Using the EdSurvey Package – Downloading data – Reading in data – Getting to know the data format – Removing special values • Explore Variable Distributions with summary2 • Subsetting the data • Retrieving data for further manipulation with getData *This publication was prepared for NCES under Contract No. ED-IES-12-D-0002 with the American Institutes for Research. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S.
  • Supplementary Table S1. ATM Alterations in the 1,661-Patient MSK-TMB Study

    Supplementary Table S1. ATM Alterations in the 1,661-Patient MSK-TMB Study

    Supplementary Table S1. ATM alterations in the 1,661-patient MSK-TMB study Cancer Type Total Mutation Fusion Bladder Cancer 215 23 0 Colorectal Cancer 110 11 0 Melanoma 320 26 0 Cancer of Unknown Primary 88 7 0 Non-Small Cell Lung Cancer 350 23 0 Esophagogastric Carcinoma 126 6 1 Breast Cancer 44 2 0 Glioma 117 3 0 Renal Cell Carcinoma 151 2 0 Head and Neck Cancer 139 1 0 Supplementary Table S2. ATM alternations in the 10,945-patient MSK-IMPACT study Cancer Type Total Mutation Deep deletion Amplification Multi-alterations Fusion Small Bowl Cancer 35 7 0 0 0 0 Skin Cancer, Non-Melaoma 148 19 1 0 0 0 Bladder Cancer 423 45 2 0 0 0 Endometrial Cancer 218 24 0 0 0 0 Hepatobiliary Cancer 355 27 0 0 0 0 Colorectal Cancer 1007 75 0 0 0 1 Mature B-Cell Neoplasms 134 8 0 0 2 0 Non-Small Cell Lung Cancer 1668 120 0 2 1 0 Melanoma 365 23 0 0 0 0 Appendiceal Cancer 79 4 0 0 0 0 Small Cell Lung Cancer 82 4 0 0 0 0 Prostate Cancer 717 25 7 1 0 0 Histiocytosis 22 1 0 0 0 0 Salivary Gland Cancer 114 5 0 0 0 0 Thyroid Cancer 231 10 0 0 0 0 Cancer of Unknown Primary 186 8 0 0 0 0 Breast Cancer 1324 48 3 2 0 0 Adrenocortical Carcinoma 25 1 0 0 0 0 Mature T and NK Neoplasms 29 1 0 0 0 0 Renal Cell Carcinoma 361 12 0 0 0 0 Head and Neck Cancer 186 14 0 0 0 1 Pancreatic Cancer 502 69 7 2 0 0 Glioma 553 14 1 0 0 0 Soft Tissue Sarcoma 443 13 1 0 0 0 Esophagogastric Cancer 341 8 1 0 0 0 Peripheral Nervous System 80 0 2 0 0 0 Germ Cell Tumor 288 2 0 1 0 1 Ovarian Can 244 2 1 0 0 0 Uterine Sarcoma 93 1 0 0 0 0 Mesothelioma 107 1 0 0 0 0 Bone Cancer 134 1 0 0 0 0 Gastrointestinal Stromal Ca 137 0 0 0 0 1 Supplementary Table S3.
  • Inhibition of the MID1 Protein Complex

    Inhibition of the MID1 Protein Complex

    Matthes et al. Cell Death Discovery (2018) 4:4 DOI 10.1038/s41420-017-0003-8 Cell Death Discovery ARTICLE Open Access Inhibition of the MID1 protein complex: a novel approach targeting APP protein synthesis Frank Matthes1,MoritzM.Hettich1, Judith Schilling1, Diana Flores-Dominguez1, Nelli Blank1, Thomas Wiglenda2, Alexander Buntru2,HannaWolf1, Stephanie Weber1,InaVorberg 1, Alina Dagane2, Gunnar Dittmar2,3,ErichWanker2, Dan Ehninger1 and Sybille Krauss1 Abstract Alzheimer’s disease (AD) is characterized by two neuropathological hallmarks: senile plaques, which are composed of amyloid-β (Aβ) peptides, and neurofibrillary tangles, which are composed of hyperphosphorylated tau protein. Aβ peptides are derived from sequential proteolytic cleavage of the amyloid precursor protein (APP). In this study, we identified a so far unknown mode of regulation of APP protein synthesis involving the MID1 protein complex: MID1 binds to and regulates the translation of APP mRNA. The underlying mode of action of MID1 involves the mTOR pathway. Thus, inhibition of the MID1 complex reduces the APP protein level in cultures of primary neurons. Based on this, we used one compound that we discovered previously to interfere with the MID1 complex, metformin, for in vivo experiments. Indeed, long-term treatment with metformin decreased APP protein expression levels and consequently Aβ in an AD mouse model. Importantly, we have initiated the metformin treatment late in life, at a time-point where mice were in an already progressed state of the disease, and could observe an improved behavioral phenotype. These 1234567890 1234567890 findings together with our previous observation, showing that inhibition of the MID1 complex by metformin also decreases tau phosphorylation, make the MID1 complex a particularly interesting drug target for treating AD.
  • Supplementary Dataset S2

    Supplementary Dataset S2

    mitochondrial translational termination MRPL28 MRPS26 6 MRPS21 PTCD3 MTRF1L 4 MRPL50 MRPS18A MRPS17 2 MRPL20 MRPL52 0 MRPL17 MRPS33 MRPS15 −2 MRPL45 MRPL30 MRPS27 AURKAIP1 MRPL18 MRPL3 MRPS6 MRPS18B MRPL41 MRPS2 MRPL34 GADD45GIP1 ERAL1 MRPL37 MRPS10 MRPL42 MRPL19 MRPS35 MRPL9 MRPL24 MRPS5 MRPL44 MRPS23 MRPS25 ITB ITB ITB ITB ICa ICr ITL original ICr ICa ITL ICa ITL original ICr ITL ICr ICa mitochondrial translational elongation MRPL28 MRPS26 6 MRPS21 PTCD3 MRPS18A 4 MRPS17 MRPL20 2 MRPS15 MRPL45 MRPL52 0 MRPS33 MRPL30 −2 MRPS27 AURKAIP1 MRPS10 MRPL42 MRPL19 MRPL18 MRPL3 MRPS6 MRPL24 MRPS35 MRPL9 MRPS18B MRPL41 MRPS2 MRPL34 MRPS5 MRPL44 MRPS23 MRPS25 MRPL50 MRPL17 GADD45GIP1 ERAL1 MRPL37 ITB ITB ITB ITB ICa ICr original ICr ITL ICa ITL ICa ITL original ICr ITL ICr ICa translational termination MRPL28 MRPS26 6 MRPS21 PTCD3 C12orf65 4 MTRF1L MRPL50 MRPS18A 2 MRPS17 MRPL20 0 MRPL52 MRPL17 MRPS33 −2 MRPS15 MRPL45 MRPL30 MRPS27 AURKAIP1 MRPL18 MRPL3 MRPS6 MRPS18B MRPL41 MRPS2 MRPL34 GADD45GIP1 ERAL1 MRPL37 MRPS10 MRPL42 MRPL19 MRPS35 MRPL9 MRPL24 MRPS5 MRPL44 MRPS23 MRPS25 ITB ITB ITB ITB ICa ICr original ICr ITL ICa ITL ICa ITL original ICr ITL ICr ICa translational elongation DIO2 MRPS18B MRPL41 6 MRPS2 MRPL34 GADD45GIP1 4 ERAL1 MRPL37 2 MRPS10 MRPL42 MRPL19 0 MRPL30 MRPS27 AURKAIP1 −2 MRPL18 MRPL3 MRPS6 MRPS35 MRPL9 EEF2K MRPL50 MRPS5 MRPL44 MRPS23 MRPS25 MRPL24 MRPS33 MRPL52 EIF5A2 MRPL17 SECISBP2 MRPS15 MRPL45 MRPS18A MRPS17 MRPL20 MRPL28 MRPS26 MRPS21 PTCD3 ITB ITB ITB ITB ICa ICr ICr ITL original ITL ICa ICa ITL ICr ICr ICa original
  • The Pennsylvania State University the Graduate School Eberly

    The Pennsylvania State University the Graduate School Eberly

    The Pennsylvania State University The Graduate School Eberly College of Science REGULATION OF MITOCHONDRIAL TRANSLATION AND OXIDATIVE PHOSPHORYLATION THROUGH REVERSIBLE ACETYLATION A Dissertation in Biochemistry, Microbiology and Molecular Biology by Hüseyin Çimen 2012 Hüseyin Çimen Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2012 The Dissertation of Hüseyin Çimen was reviewed and approved* by the following: Emine C. Koc Assistant Professor of Biochemistry and Molecular Biology Dissertation Co-adviser Co-chair of Committee Hasan Koc Assistant Professor of Natural Sciences Dissertation Co-adviser Co-chair of Committee Craig E. Cameron Paul Berg Professor of Biochemistry and Molecular Biology Associate Department Head for Research and Graduate Education Joseph C. Reese Professor of Biochemistry and Molecular Biology Teh-hui Kao Professor of Biochemistry and Molecular Biology Tae-Hee Lee Assistant Professor of Chemistry and the Huck Institute of the Life Sciences Craig E. Cameron Paul Berg Professor of Biochemistry and Molecular Biology Associate Department Head of the Department of Biochemistry and Molecular Biology iii ABSTRACT In a eukaryotic cell, mitochondria provide energy in the form of ATP through oxidative phosphorylation (OXPHOS), which consists of five electron transport chain complexes embedded in the inner membrane of mitochondria. Human mitochondria have their own genome and transcription/translation system to synthesize mitochondrially encoded thirteen proteins of respiratory chain complexes. We investigated how acetylation of ribosomal proteins regulates translation and energy production in mitochondria since reversible acetylation of mitochondrial proteins was found to be critical for maintaining energy homeostasis. We identified mitochondrial ribosomal protein L10 (MRPL10) as the major acetylated ribosomal protein in mammalian mitochondria with two-dimensional gel electrophoresis followed by tandem mass spectrometry and immunoblotting analyses.