Using a Combinatorial Peptide Ligand Library to Reduce the Dynamic Range of Protein Concentrations in Complicated Biological Samples
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Recruitment of Genes and Enzymes Conferring Resistance to the Nonnatural Toxin Bromoacetate
Recruitment of genes and enzymes conferring resistance to the nonnatural toxin bromoacetate Kevin K. Desai and Brian G. Miller1 Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL 32306-4390 Edited* by Richard Wolfenden, University of North Carolina, Chapel Hill, NC, and approved August 24, 2010 (received for review May 28, 2010) Microbial niches contain toxic chemicals capable of forcing organ- tance of a naïve bacterial population can play a role in combating isms into periods of intense natural selection to afford survival. the toxicity of a nonnatural small-molecule. Revealing the reser- Elucidating the mechanisms by which microbes evade environmen- voir of intrinsic resistance genes that are subject to evolutionary tal threats has direct relevance for understanding and combating recruitment promises to aid our understanding of the processes the rise of antibiotic resistance. In this study we used a toxic small- leading to the emergence of antibiotic resistant pathogens. molecule, bromoacetate, to model the selective pressures imposed We sought to identify the full spectrum of bromoacetate resis- by antibiotics and anthropogenic toxins. We report the results tance mechanisms available to the model bacterium, Escherichia of genetic selection experiments that identify nine genes from coli. The reactivity of bromoacetate is likely to mimic that of elec- Escherichia coli whose overexpression affords survival in the trophilic natural products as well as anthropogenic environmen- presence of a normally lethal concentration of bromoacetate. Eight tal contaminants that microbes may encounter. The clinically of these genes encode putative transporters or transmembrane significant natural antibiotic fosfomycin, and the fungal natural proteins, while one encodes the essential peptidoglycan biosyn- product terreic acid, are electrophilic molecules that both target N thetic enzyme, UDP- -acetylglucosamine enolpyruvoyl transferase an essential nucleophilic cysteine residue in bacteria (8, 9). -
Calcium-Induced Conformational Changes in the Regulatory Domain of the Human Mitochondrial ATP-Mg/Pi Carrier
Biochimica et Biophysica Acta 1847 (2015) 1245–1253 Contents lists available at ScienceDirect Biochimica et Biophysica Acta journal homepage: www.elsevier.com/locate/bbabio Calcium-induced conformational changes in the regulatory domain of the human mitochondrial ATP-Mg/Pi carrier Steven P.D. Harborne, Jonathan J. Ruprecht, Edmund R.S. Kunji ⁎ The Medical Research Council, Mitochondrial Biology Unit, Cambridge Biomedical Campus, Wellcome Trust/MRC Building, Hills Road, Cambridge CB2 0XY, UK article info abstract Article history: The mitochondrial ATP-Mg/Pi carrier imports adenine nucleotides from the cytosol into the mitochondrial matrix Received 24 April 2015 and exports phosphate. The carrier is regulated by the concentration of cytosolic calcium, altering the size of the Received in revised form 15 June 2015 adenine nucleotide pool in the mitochondrial matrix in response to energetic demands. The protein consists of Accepted 6 July 2015 three domains; (i) the N-terminal regulatory domain, which is formed of two pairs of fused calcium-binding EF- Available online 9 July 2015 hands, (ii) the C-terminal mitochondrial carrier domain, which is involved in transport, and (iii) a linker region α Keywords: with an amphipathic -helix of unknown function. The mechanism by which calcium binding to the regulatory do- Calcium regulation mechanism main modulates substrate transport in the carrier domain has not been resolved. Here, we present two new crystal EF-hand conformational change structures of the regulatory domain of the human isoform 1. Careful analysis by SEC confirmed that although the SCaMC regulatory domain crystallised as dimers, full-length ATP-Mg/Pi carrier is monomeric. Therefore, the ATP-Mg/Pi Adenine nucleotide translocase carrier must have a different mechanism of calcium regulation than the architecturally related aspartate/glutamate Regulation of adenine nucleotides carrier, which is dimeric. -
RNA Expression Patterns in Serum Microvesicles from Patients With
Noerholm et al. BMC Cancer 2012, 12:22 http://www.biomedcentral.com/1471-2407/12/22 RESEARCHARTICLE Open Access RNA expression patterns in serum microvesicles from patients with glioblastoma multiforme and controls Mikkel Noerholm1,2*, Leonora Balaj1, Tobias Limperg1,3, Afshin Salehi1,4, Lin Dan Zhu1, Fred H Hochberg1, Xandra O Breakefield1, Bob S Carter1,4 and Johan Skog1,2 Abstract Background: RNA from exosomes and other microvesicles contain transcripts of tumour origin. In this study we sought to identify biomarkers of glioblastoma multiforme in microvesicle RNA from serum of affected patients. Methods: Microvesicle RNA from serum from patients with de-novo primary glioblastoma multiforme (N = 9) and normal controls (N = 7) were analyzed by microarray analysis. Samples were collected according to protocols approved by the Institutional Review Board. Differential expressions were validated by qRT-PCR in a separate set of samples (N = 10 in both groups). Results: Expression profiles of microvesicle RNA correctly separated individuals in two groups by unsupervised clustering. The most significant differences pertained to down-regulated genes (121 genes > 2-fold down) in the glioblastoma multiforme patient microvesicle RNA, validated by qRT-PCR on several genes. Overall, yields of microvesicle RNA from patients was higher than from normal controls, but the additional RNA was primarily of size < 500 nt. Gene ontology of the down-regulated genes indicated these are coding for ribosomal proteins and genes related to ribosome production. Conclusions: Serum microvesicle RNA from patients with glioblastoma multiforme has significantly down- regulated levels of RNAs coding for ribosome production, compared to normal healthy controls, but a large overabundance of RNA of unknown origin with size < 500 nt. -
Supplemental Figure 1. Vimentin
Double mutant specific genes Transcript gene_assignment Gene Symbol RefSeq FDR Fold- FDR Fold- FDR Fold- ID (single vs. Change (double Change (double Change wt) (single vs. wt) (double vs. single) (double vs. wt) vs. wt) vs. single) 10485013 BC085239 // 1110051M20Rik // RIKEN cDNA 1110051M20 gene // 2 E1 // 228356 /// NM 1110051M20Ri BC085239 0.164013 -1.38517 0.0345128 -2.24228 0.154535 -1.61877 k 10358717 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 /// BC 1700025G04Rik NM_197990 0.142593 -1.37878 0.0212926 -3.13385 0.093068 -2.27291 10358713 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 1700025G04Rik NM_197990 0.0655213 -1.71563 0.0222468 -2.32498 0.166843 -1.35517 10481312 NM_027283 // 1700026L06Rik // RIKEN cDNA 1700026L06 gene // 2 A3 // 69987 /// EN 1700026L06Rik NM_027283 0.0503754 -1.46385 0.0140999 -2.19537 0.0825609 -1.49972 10351465 BC150846 // 1700084C01Rik // RIKEN cDNA 1700084C01 gene // 1 H3 // 78465 /// NM_ 1700084C01Rik BC150846 0.107391 -1.5916 0.0385418 -2.05801 0.295457 -1.29305 10569654 AK007416 // 1810010D01Rik // RIKEN cDNA 1810010D01 gene // 7 F5 // 381935 /// XR 1810010D01Rik AK007416 0.145576 1.69432 0.0476957 2.51662 0.288571 1.48533 10508883 NM_001083916 // 1810019J16Rik // RIKEN cDNA 1810019J16 gene // 4 D2.3 // 69073 / 1810019J16Rik NM_001083916 0.0533206 1.57139 0.0145433 2.56417 0.0836674 1.63179 10585282 ENSMUST00000050829 // 2010007H06Rik // RIKEN cDNA 2010007H06 gene // --- // 6984 2010007H06Rik ENSMUST00000050829 0.129914 -1.71998 0.0434862 -2.51672 -
A Proteomic Approach to Uncover Neuroprotective Mechanisms of Oleocanthal Against Oxidative Stress
International Journal of Molecular Sciences Article A Proteomic Approach to Uncover Neuroprotective Mechanisms of Oleocanthal against Oxidative Stress Laura Giusti 1,†, Cristina Angeloni 2,†, Maria Cristina Barbalace 3, Serena Lacerenza 4, Federica Ciregia 5, Maurizio Ronci 6 ID , Andrea Urbani 7, Clementina Manera 4, Maria Digiacomo 4 ID , Marco Macchia 4, Maria Rosa Mazzoni 4, Antonio Lucacchini 1 ID and Silvana Hrelia 3,* ID 1 Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy; [email protected] (L.G.); [email protected] (A.L.) 2 School of Pharmacy, University of Camerino, 62032 Camerino, Italy; [email protected] 3 Department for Life Quality Studies, Alma Mater Studiorum, University of Bologna, 47921 Rimini, Italy; [email protected] 4 Department of Pharmacy, University of Pisa, 56126 Pisa, Italy; [email protected] (S.L.); [email protected] (C.M.); [email protected] (M.D.); [email protected] (M.M.); [email protected] (M.R.M.) 5 Department of Rheumatology, GIGA Research, Centre Hospitalier Universitaire (CHU) de Liège, University of Liège, 4000 Liège, Belgium; [email protected] 6 Department of Medical, Oral and Biotechnological Sciences, University G. d’Annunzio of Chieti-Pescara, 65127 Pescara, Italy; [email protected] 7 Institute of Biochemistry and Clinical Biochemistry, Catholic University, 00198 Rome, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-051-209-1235 † These authors contributed equally to this work. Received: 3 July 2018; Accepted: 1 August 2018; Published: 8 August 2018 Abstract: Neurodegenerative diseases represent a heterogeneous group of disorders that share common features like abnormal protein aggregation, perturbed Ca2+ homeostasis, excitotoxicity, impairment of mitochondrial functions, apoptosis, inflammation, and oxidative stress. -
BMC Genomics Biomed Central
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Springer - Publisher Connector BMC Genomics BioMed Central Research article Open Access Dual activation of pathways regulated by steroid receptors and peptide growth factors in primary prostate cancer revealed by Factor Analysis of microarray data Juan Jose Lozano1,2,5, Marta Soler3, Raquel Bermudo3, David Abia1, Pedro L Fernandez4, Timothy M Thomson*3 and Angel R Ortiz*1,2 Address: 1Bioinformatics Unit, Centro de Biología Molecular "Severo Ochoa" (CSIC-UAM), Universidad Autónoma de Madrid, Cantoblanco, 28049 Madrid, Spain, 2Department of Physiology and Biophysics, Mount Sinai School of Medicine, One Gustave Levy Pl., New York, NY 10029, USA, 3Instituto de Biología Molecular, Consejo Superior de Investigaciones Científicas, c. Jordi Girona 18–26, 08034 Barcelona, Spain, 4Departament de Anatomía Patològica, Hospital Clínic, and Institut de Investigacions Biomèdiques August Pi i Sunyer, c. Villarroel 170, 08036 Barcelona, Spain and 5Center for Genome Regulation, Barcelona (Spain) Email: Juan Jose Lozano - [email protected]; Marta Soler - [email protected]; Raquel Bermudo - [email protected]; David Abia - [email protected]; Pedro L Fernandez - [email protected]; Timothy M Thomson* - [email protected]; Angel R Ortiz* - [email protected] * Corresponding authors Published: 17 August 2005 Received: 17 January 2005 Accepted: 17 August 2005 BMC Genomics 2005, 6:109 doi:10.1186/1471-2164-6-109 This article is available from: http://www.biomedcentral.com/1471-2164/6/109 © 2005 Lozano et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -
Supplementary Materials
Supplementary Materials COMPARATIVE ANALYSIS OF THE TRANSCRIPTOME, PROTEOME AND miRNA PROFILE OF KUPFFER CELLS AND MONOCYTES Andrey Elchaninov1,3*, Anastasiya Lokhonina1,3, Maria Nikitina2, Polina Vishnyakova1,3, Andrey Makarov1, Irina Arutyunyan1, Anastasiya Poltavets1, Evgeniya Kananykhina2, Sergey Kovalchuk4, Evgeny Karpulevich5,6, Galina Bolshakova2, Gennady Sukhikh1, Timur Fatkhudinov2,3 1 Laboratory of Regenerative Medicine, National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia 2 Laboratory of Growth and Development, Scientific Research Institute of Human Morphology, Moscow, Russia 3 Histology Department, Medical Institute, Peoples' Friendship University of Russia, Moscow, Russia 4 Laboratory of Bioinformatic methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia 5 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia 6 Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia Figure S1. Flow cytometry analysis of unsorted blood sample. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S2. Flow cytometry analysis of unsorted liver stromal cells. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S3. MiRNAs expression analysis in monocytes and Kupffer cells. Full-length of heatmaps are presented. -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Transcriptomic and Proteomic Profiling Provides Insight Into
BASIC RESEARCH www.jasn.org Transcriptomic and Proteomic Profiling Provides Insight into Mesangial Cell Function in IgA Nephropathy † † ‡ Peidi Liu,* Emelie Lassén,* Viji Nair, Celine C. Berthier, Miyuki Suguro, Carina Sihlbom,§ † | † Matthias Kretzler, Christer Betsholtz, ¶ Börje Haraldsson,* Wenjun Ju, Kerstin Ebefors,* and Jenny Nyström* *Department of Physiology, Institute of Neuroscience and Physiology, §Proteomics Core Facility at University of Gothenburg, University of Gothenburg, Gothenburg, Sweden; †Division of Nephrology, Department of Internal Medicine and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan; ‡Division of Molecular Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan; |Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden; and ¶Integrated Cardio Metabolic Centre, Karolinska Institutet Novum, Huddinge, Sweden ABSTRACT IgA nephropathy (IgAN), the most common GN worldwide, is characterized by circulating galactose-deficient IgA (gd-IgA) that forms immune complexes. The immune complexes are deposited in the glomerular mesangium, leading to inflammation and loss of renal function, but the complete pathophysiology of the disease is not understood. Using an integrated global transcriptomic and proteomic profiling approach, we investigated the role of the mesangium in the onset and progression of IgAN. Global gene expression was investigated by microarray analysis of the glomerular compartment of renal biopsy specimens from patients with IgAN (n=19) and controls (n=22). Using curated glomerular cell type–specific genes from the published literature, we found differential expression of a much higher percentage of mesangial cell–positive standard genes than podocyte-positive standard genes in IgAN. Principal coordinate analysis of expression data revealed clear separation of patient and control samples on the basis of mesangial but not podocyte cell–positive standard genes. -
Appendix 2. Significantly Differentially Regulated Genes in Term Compared with Second Trimester Amniotic Fluid Supernatant
Appendix 2. Significantly Differentially Regulated Genes in Term Compared With Second Trimester Amniotic Fluid Supernatant Fold Change in term vs second trimester Amniotic Affymetrix Duplicate Fluid Probe ID probes Symbol Entrez Gene Name 1019.9 217059_at D MUC7 mucin 7, secreted 424.5 211735_x_at D SFTPC surfactant protein C 416.2 206835_at STATH statherin 363.4 214387_x_at D SFTPC surfactant protein C 295.5 205982_x_at D SFTPC surfactant protein C 288.7 1553454_at RPTN repetin solute carrier family 34 (sodium 251.3 204124_at SLC34A2 phosphate), member 2 238.9 206786_at HTN3 histatin 3 161.5 220191_at GKN1 gastrokine 1 152.7 223678_s_at D SFTPA2 surfactant protein A2 130.9 207430_s_at D MSMB microseminoprotein, beta- 99.0 214199_at SFTPD surfactant protein D major histocompatibility complex, class II, 96.5 210982_s_at D HLA-DRA DR alpha 96.5 221133_s_at D CLDN18 claudin 18 94.4 238222_at GKN2 gastrokine 2 93.7 1557961_s_at D LOC100127983 uncharacterized LOC100127983 93.1 229584_at LRRK2 leucine-rich repeat kinase 2 HOXD cluster antisense RNA 1 (non- 88.6 242042_s_at D HOXD-AS1 protein coding) 86.0 205569_at LAMP3 lysosomal-associated membrane protein 3 85.4 232698_at BPIFB2 BPI fold containing family B, member 2 84.4 205979_at SCGB2A1 secretoglobin, family 2A, member 1 84.3 230469_at RTKN2 rhotekin 2 82.2 204130_at HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 81.9 222242_s_at KLK5 kallikrein-related peptidase 5 77.0 237281_at AKAP14 A kinase (PRKA) anchor protein 14 76.7 1553602_at MUCL1 mucin-like 1 76.3 216359_at D MUC7 mucin 7, -
MALE Protein Name Accession Number Molecular Weight CP1 CP2 H1 H2 PDAC1 PDAC2 CP Mean H Mean PDAC Mean T-Test PDAC Vs. H T-Test
MALE t-test t-test Accession Molecular H PDAC PDAC vs. PDAC vs. Protein Name Number Weight CP1 CP2 H1 H2 PDAC1 PDAC2 CP Mean Mean Mean H CP PDAC/H PDAC/CP - 22 kDa protein IPI00219910 22 kDa 7 5 4 8 1 0 6 6 1 0.1126 0.0456 0.1 0.1 - Cold agglutinin FS-1 L-chain (Fragment) IPI00827773 12 kDa 32 39 34 26 53 57 36 30 55 0.0309 0.0388 1.8 1.5 - HRV Fab 027-VL (Fragment) IPI00827643 12 kDa 4 6 0 0 0 0 5 0 0 - 0.0574 - 0.0 - REV25-2 (Fragment) IPI00816794 15 kDa 8 12 5 7 8 9 10 6 8 0.2225 0.3844 1.3 0.8 A1BG Alpha-1B-glycoprotein precursor IPI00022895 54 kDa 115 109 106 112 111 100 112 109 105 0.6497 0.4138 1.0 0.9 A2M Alpha-2-macroglobulin precursor IPI00478003 163 kDa 62 63 86 72 14 18 63 79 16 0.0120 0.0019 0.2 0.3 ABCB1 Multidrug resistance protein 1 IPI00027481 141 kDa 41 46 23 26 52 64 43 25 58 0.0355 0.1660 2.4 1.3 ABHD14B Isoform 1 of Abhydrolase domain-containing proteinIPI00063827 14B 22 kDa 19 15 19 17 15 9 17 18 12 0.2502 0.3306 0.7 0.7 ABP1 Isoform 1 of Amiloride-sensitive amine oxidase [copper-containing]IPI00020982 precursor85 kDa 1 5 8 8 0 0 3 8 0 0.0001 0.2445 0.0 0.0 ACAN aggrecan isoform 2 precursor IPI00027377 250 kDa 38 30 17 28 34 24 34 22 29 0.4877 0.5109 1.3 0.8 ACE Isoform Somatic-1 of Angiotensin-converting enzyme, somaticIPI00437751 isoform precursor150 kDa 48 34 67 56 28 38 41 61 33 0.0600 0.4301 0.5 0.8 ACE2 Isoform 1 of Angiotensin-converting enzyme 2 precursorIPI00465187 92 kDa 11 16 20 30 4 5 13 25 5 0.0557 0.0847 0.2 0.4 ACO1 Cytoplasmic aconitate hydratase IPI00008485 98 kDa 2 2 0 0 0 0 2 0 0 - 0.0081 - 0.0 -
In This Table Protein Name, Uniprot Code, Gene Name P-Value
Supplementary Table S1: In this table protein name, uniprot code, gene name p-value and Fold change (FC) for each comparison are shown, for 299 of the 301 significantly regulated proteins found in both comparisons (p-value<0.01, fold change (FC) >+/-0.37) ALS versus control and FTLD-U versus control. Two uncharacterized proteins have been excluded from this list Protein name Uniprot Gene name p value FC FTLD-U p value FC ALS FTLD-U ALS Cytochrome b-c1 complex P14927 UQCRB 1.534E-03 -1.591E+00 6.005E-04 -1.639E+00 subunit 7 NADH dehydrogenase O95182 NDUFA7 4.127E-04 -9.471E-01 3.467E-05 -1.643E+00 [ubiquinone] 1 alpha subcomplex subunit 7 NADH dehydrogenase O43678 NDUFA2 3.230E-04 -9.145E-01 2.113E-04 -1.450E+00 [ubiquinone] 1 alpha subcomplex subunit 2 NADH dehydrogenase O43920 NDUFS5 1.769E-04 -8.829E-01 3.235E-05 -1.007E+00 [ubiquinone] iron-sulfur protein 5 ARF GTPase-activating A0A0C4DGN6 GIT1 1.306E-03 -8.810E-01 1.115E-03 -7.228E-01 protein GIT1 Methylglutaconyl-CoA Q13825 AUH 6.097E-04 -7.666E-01 5.619E-06 -1.178E+00 hydratase, mitochondrial ADP/ATP translocase 1 P12235 SLC25A4 6.068E-03 -6.095E-01 3.595E-04 -1.011E+00 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 MIC J3QTA6 CHCHD6 1.090E-04 -5.913E-01 2.124E-03 -5.948E-01 Protein kinase C and casein Q9BY11 PACSIN1 3.837E-03 -5.863E-01 3.680E-06 -1.824E+00 kinase substrate in neurons protein 1 Tubulin polymerization- O94811 TPPP 6.466E-03 -5.755E-01 6.943E-06 -1.169E+00 promoting protein MIC C9JRZ6 CHCHD3 2.912E-02 -6.187E-01 2.195E-03 -9.781E-01 Mitochondrial 2-