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opencc-by-4.0Nov 2024View details →

Safety and Efficacy of Mesenchymal Stromal Cells Mitochondria Transplantation as a Cell-Free Therapy for Osteoarthritis

Abstract Objective The inflammatory responses from synovial fibroblasts and macrophages and the mitochondrial dysfunction in chondrocytes leading to oxidative stress, disrupt extracellular matrix (ECM) homeostasis and accelerate the deterioration process of articular cartilage in osteoarthritis (OA).  In the last years, it has been proposed that mesenchymal stromal cells (MSC) transfer their functional mitochondria to damaged cells in response to cellular stress, becoming one of the mechanisms underpinning their therapeutic effects. Therefore, we hypothesize that a novel cell-free treatment for OA could involve direct mitochondria transplantation, leading to the restoration of both cellular and mitochondrial homeostasis. Methods Mitochondria were isolated from Umbilical Cord (UC)-MSC (Mito-MSC) and characterized based on their morphology, phenotype, functions, and their ability to be internalized by different articular cells. Furthermore, the transcriptional changes following mitochondrial uptake by chondrocytes were evaluated using an Affymetrix analysis, Lastly, the dose dependance therapeutic efficacy, biodistribution and immunogenicity of Mito-MSC were assessed in vivo, through an intra-articular injection in male C57BL6 mice in a collagenase-induced OA (CIOA) model. Results Our findings demonstrate the functional integrity of Mito-MSC and their ability to be efficiently transferred into chondrocytes, synovial macrophages, and synovial fibroblasts. Moreover, the transcriptomic analysis showed the upregulation of genes involved in stress such as DNA reparative machinery and inflammatory antiviral responses.  Finally, Mito-MSC transplantation yielded significant reductions in joint mineralization, a hallmark of OA progression, as well as improvements in OA-related histological signs, with the lower dose exhibiting better therapeutic efficacy.  Furthermore, Mito-MSC were detected within the knee joint for up to 24 hours post-injection without eliciting an inflammatory response in CIOA mice. Conclusion  Collectively, our results reveal that mitochondria derived from MSC are transferred to key articular cells and are retained in the joint without generating an inflammatory immune response mitigating articular cartilage degradation in OA, probably through a restorative effect trigger by the stress antiviral response within OA chondrocytes.

opencc-by-4.0Apr 2022View details →

Structural conversion of α-synuclein at the mitochondria induces neuronal toxicity; Image data

Lists of image sets included in "Structural conversion of α-synuclein at the mitochondria induces neuronal toxicity"   Duplex-1 and Duplex-2 Images Amyloid Fibril TIRFM Images (SNCA-A53T ImagesTIRF Images) TEM Fibril Images DLS Images  SMLM Images 1 & 2   CLEM Images <ul> <li>FIB SEM Images (videos)</li> <li>TEM Images </li> </ul>   Live-cell imaging <ul> <li>Superoxide Images</li> <li>MitoTracker® Red Images</li> <li>Membrane Potential (TMRM) Images </li> <li>Ca 2+ Images</li> <li>NADH Autofluorescence Images</li> <li>Cell Death Images</li> <li>Amytracker Images</li> <li>Cardiolipin Images</li> <li>FRET Images</li> </ul>

opencc-by-4.0Aug 2021View details →

Global ubiquitylation analysis of mitochondria in primary neurons identifies endogenous Parkin targets following activation of PINK1

How activation of PINK1 and Parkin leads to elimination of damaged mitochondria by mitophagy is largely based on cell lines with few studies in neurons. Herein we have undertaken proteomic analysis of mitochondria from mouse neurons to identify ubiquitylated substrates of endogenous Parkin. Comparative analysis with human iNeuron datasets revealed a subset of 49 PINK1 activation-dependent diGLY sites in 22 proteins conserved across mouse and human systems. We employ reconstitution assays to demonstrate direct ubiquitylation by Parkin in vitro. We also identified a subset of cytoplasmic proteins recruited to mitochondria that undergo PINK1 and Parkin independent ubiquitylation indicating the presence of alternate ubiquitin E3 ligase  pathways that are activated by mitochondrial depolarisation in neurons. Finally we have developed an online resource to search for ubiquitin sites and enzymes in mitochondria of neurons, MitoNUb. These findings will aid future studies to understand Parkin activation in neuronal subtypes.   FILE DECRIPTIONS Figure 1C: Immunoblots for PINK1 signaling in PINK1 WT and KO mouse cortical neurons. Scans of X-ray film: <ul> <li>GAPDH shown in Figure1C_GAPDH.tif</li> <li>Parkin shown in Figure1C_Parkin.tif</li> <li>Phospho-Ser65 Parkin shown in Figure1C_ParkinPSer65.tif</li> <li>PINK1 shown in Figure1C_PINK1.tif (immunoprecipitation)</li> <li>Rab8A shown in Figure1C_Rab8A.tif</li> <li>Phospho-Ser111 Rab8A shown in Figure1C_Rab8APSer111.tif</li> <li>Ubiquitin shown in Figure1C_ S10A_Ubiquitin.tif (same blot used for Figure_S10A, Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in Figure1C_S10A_UbiquitinPSer65.tif (same blot used for Figure_S10A, Halo-multiDSK pull-down)</li> </ul>   Figure 4A: Immunoblots for time-course of Parkin-dependent substrates in C57BL/6J neurons. Scans of X-ray film: <ul> <li>CISD1 shown in Figure4A_CISD1.tif (Halo-multiDSK pull-down) <ul> <li>  Figure4A_INPUT_CISD1.tif (INPUT)</li> </ul> </li> <li>CPT1α shown in Figure4A_ CPT1a.tif (Halo-multiDSK pull-down) <ul> <li>  Figure4A_INPUT_CPT1a.tif (INPUT)</li> </ul> </li> <li>Ubiquitin shown in Figure4A_Ubiquitin.tif (Halo-multiDSK pull-down) <ul> <li>  Figure4A_INPUT_Ubiquitin.tif (INPUT)</li> </ul> </li> <li>Phospho-Ser65 Ubiquitin shown in Figure4_UbiquitinPSer65.tif (Halo-multiDSK pull-down)</li> <li>GAPDH Shown in Figure4A_INPUT_GAPDH.tif (INPUT)</li> </ul> Figure 4B: Immunoblots for validation of Parkin-dependent substrates in PARKIN WT and KO neurons. Scans of X-ray film: <ul> <li>CISD1 shown in Figure4B_CISD1.tif (Halo-multiDSK pull-down) <ul> <li>(bottom blot) Figure4B_INPUT_CPT1a_CISD1.tif (INPUT)</li> </ul> </li> <li>CPT1α shown in Figure4B_ CPT1a.tif (Halo-multiDSK pull-down) <ul> <li>(top blot)  Figure4B_INPUT_CPT1a_CISD1.tif ( (INPUT)</li> </ul> </li> <li>Ubiquitin shown in Figure4B_Ubiquitin.tif (Halo-multiDSK pull-down) <ul> <li>  Figure4B_INPUT_Ubiquitin.tif (INPUT)</li> </ul> </li> <li>Phospho-Ser65 Ubiquitin shown in Figure4B_UbiquitinPSer65.tif (Halo-multiDSK pull-down)</li> <li>GAPDH Shown in Figure4B_INPUT_GAPDH.tif (INPUT)</li> </ul>   Figure 5A: Immunoblots for in vitro reconstitution assay in PINK1 WT and KO mouse embryonic fibroblasts. Scans of X-ray film: <ul> <li>CISD1shown in Figure5A_CISD1.tif</li> <li>CPT1α shown in Figure5A_ CPT1a.tif</li> <li>CYB5B shown in Figure5A_CYB5B.tif</li> <li>HK1 shown in Figure5A_HK1.tif</li> <li>MFN2 shown in Figure5A_MFN2.tif</li> <li>VDAC shown in Figure5A_VDAC.tif</li> <li>Phospho-Ser65 Parkin shown in Figure5A_ParkinPSer65.tif</li> <li>Parkin shown in Figure5A_Parkin.tif</li> <li>Ubiquitin shown in Figure5A_Ubiquitin.tif</li> <li>Phospho-Ser65 Ubiquitin shown in Figure5A_UbiquitinPSer65.tif </li> </ul>   Figure 5B: Immunoblots for validation of Parkin-dependent substrates in in vitro studies. Scans of Western blots (LI-COR): <ul> <li>CPT1α shown in Figure5B_ CPT1 α_800nm.tif</li> <li>Flag-Ub (CPT1α) shown in Figure5B_ CPT1 α_Flag_800nm.tif</li> <li>His (CPT1α) shown in Figure5B_ CPT1 α_His_800nm.tif</li> </ul>   <ul> <li>Miro1 shown in Figure5B_Miro1_800nm.tif</li> <li>Flag-Ub (Miro1) shown in Figure5B_ Miro1_Flag_800nm.tif</li> <li>His (Miro1) shown in Figure5B_ Miro1_His_800nm.tif</li> </ul>   Figure 5C: Immunoblots for time-course of CPT1α ubiquitylation in in vitro studies. Scans of Western blots (LI-COR): <ul> <li>CPT1α shown in top blot: Figure5C_Flag_CPT1α_800nm.tif</li> <li>Flag-Ub shown in bottom blot: Figure5C_Flag_CPT1α_800nm.tif</li> <li>His shown in Figure5C_ CPT1 α_His_800nm.tif</li> </ul>   Figure S3A: Immunoblots for PINK1 signaling in C57BL/6J neurons. Scans of X-ray film: <ul> <li>GAPDH shown in FigureS3A_GAPDH.tif</li> <li>Parkin shown in FigureS3A_Parkin.tif</li> <li>Phospho-Ser65 Parkin shown in FigureS3A_ParkinPSer65.tif</li> <li>PINK1 shown in FigureS3A_PINK1.tif (immunoprecipitation)</li> <li>Rab8A shown in FigureS3A_Rab8A.tif</li> <li>Phospho-Ser111 Rab8A shown in FigureS3A_Rab8APSer111.tif</li> <li>Ubiquitin shown in FigureS3A_Ubiquitin.tif (same blot used for Figure_4A, Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in FigureS3A_UbiquitinPSer65.tif (same blot used for Figure_4A, Halo-multiDSK pull-down)</li> <li>Phospho-Ser111 Rab8A and Phospho-Ser65 Parkin also shown in FigureS3A_ParkinPSer65_Rab8APSer111.tif</li> </ul>   Figure S3B: Immunoblots for comparison of Halo-multiDSK and Halo-TUBE in  C57BL/6J neurons. <ul> <li>Ubiquitin shown in FigureS9_S3_Ubiquitin.tif (same blot used for Figure_S9)</li> <li>Phospho-Ser65 Ubiquitin shown in FigureS9_S3_UbiquitinPSer65.tif (same blot used for Figure_S9)</li> </ul>   Figure S3C: Immunoblots for PINK1 signaling in Parkin WT and KO mouse cortical neurons. Scans of X-ray film: <ul> <li>GAPDH shown in FigureS3C_GAPDH.tif</li> <li>Parkin shown in FigureS3C_Parkin.tif</li> <li>Phospho-Ser65 Parkin shown in FigureS3C_ParkinPSer65.tif</li> <li>PINK1 shown in FigureS3C_PINK1.tif (immunoprecipitation)</li> <li>Rab8A shown in FigureS3C_Rab8A.tif</li> <li>Phospho-Ser111 Rab8A shown in FigureS3C_Rab8APSer111.tif</li> <li>Ubiquitin shown in FigureS3C_ Ubiquitin.tif (same blot used for Figure_4B, Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in FigureS3C_ UbiquitinPSer65.tif (same blot used for Figure_4B, Halo-multiDSK pull-down)</li> </ul>   Figure S4: Immunoblots for PINK-Parkin signaling in VPS35 D620N mouse cortical neurons. Scans of X-ray film: <ul> <li>GAPDH shown in FigureS4_GAPDH.tif</li> <li>Parkin shown in FigureS4_Parkin.tif</li> <li>Phospho-Ser65 Parkin shown in FigureS4_ParkinPSer65.tif</li> <li>Rab8A shown in FigureS4_Rab8A.tif</li> <li>Phospho-Ser111 Rab8A shown in FigureS4_Rab8APSer111.tif</li> <li>CISD1 shown in FigureS4_ CISD1.tif (Halo-UBQLN1 pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in FigureS4_ UbiquitinPSer65.tif (Halo-UBQLN1 pull-down)</li> <li>VPS35 shown in FigureS4_VPS35.tif</li> </ul> Scan of Memcode shown in FigureS4_Memcode.tif   Figure S6: Immunoblots for biochemical analysis in C56BL/6J mouse cortical neurons. Scans of X-ray film: <ul> <li>GAPDH shown in FigureS6_GAPDH.tif</li> <li>Phospho-Ser65 Ubiquitin shown in FigureS6_ UbiquitinPSer65.tif </li> </ul>   Figure S8: Immunoblots for biochemical analysis in PINK1 WT and KO mouse cortical neurons. Scans of X-ray film: <ul> <li>GAPDH shown in FigureS8_GAPDH.tif</li> <li>Phospho-Ser65 Ubiquitin shown in FigureS8_ UbiquitinPSer65.tif</li> </ul>   Figure S9: Immunoblots for biochemical analysis of ubiquitylated target in C56BL/6J mouse cortical neurons. Membrane-enriched lysate subjected to ubiquitin capture using UBQLN1(TUBE), multiDSK and mutant multiDSK pull-down (Illustration of sample loading in figures FigureS9_ACSL6,_MFN2, _UbiquitinPSer65, _NAV1.7). Scans of X-ray film: <ul> <li>Ubiquitin shown in FigureS9_S3_Ubiquitin.tif (same blot used for Figure_S3B)</li> <li>Phospho-Ser65 Ubiquitin shown in FigureS9_S3_UbiquitinPSer65.tif (same blot used for Figure_S3B)</li> <li>ABCD3 shown in FigureS9_ABCD3.tif</li> <li>ACSL1shown in FigureS9_ ACSL1.tif</li> <li>ACSL6 shown in FigureS9_ACSL6.tif</li> <li>AGPAT5 shown in FigureS9_AGPAT5.tif</li> <li>ARHGAP33 shown in FigureS9_ARHGAP33.tif</li> <li>ATAD1 shown in FigureS9_ATAD1.tif</li> <li>CAD shown in FigureS9_ CAD.tif</li> <li>CAMK2A shown in FigureS9_CAMK2A.tif</li> <li>CAMK2B shown in FigureS9_CAMK2B.tif</li> <li>CDK16 shown in FigureS9_CDK16.tif</li> <li>CISD1shown in FigureS9_CISD1.tif</li> <li>CNN3 shown in FigureS9_CNN3.tif</li> <li>CPT1α shown in FigureS9_ CPT1A.tif</li> <li>CYB5B shown in FigureS9_CYB5B.tif</li> <li>CYB5R3 shown in FigureS9_CYB5R3.tif</li> <li>DCAKD shown in FigureS9_DCAKD.tif</li> <li>DCAMKL2 shown in FigureS9_DCAMKL2.tif</li> <li>FAM213A shown in FigureS9_FAM213A.tif</li> <li>FBXO41 shown in FigureS9_FBXO41.tif</li> <li>GK shown in FigureS9_GK.tif</li> <li>HK1 shown in FigureS9_HK1.tif</li> <li>HSDL1 shown in FigureS9_HSDL1.tif</li> <li>MAO-A shown in FigureS9_MAOA.tif</li> <li>MAO-B shown in FigureS9_MAOB.tif</li> <li>MAPRE2 shown in FigureS9_MAPRE2.tif</li> <li>MARC2 shown in FigureS9_MARC2.tif</li> <li>MFN1 shown in FigureS9_MFN1.tif</li> <li>MFN2 shown in FigureS9_MFN2.tif</li> <li>NAV1.7 shown in FigureS9_NAV1.7.tif</li> <li>P23 shown in FigureS9_p23.tif</li> <li>PRKCG shown in FigureS9_PRKCG.tif</li> <li>RAB5C shown in FigureS9_Rab5c.tif</li> <li>RHOT2 shown in FigureS9_RHOT2.tif</li> <li>RIMS4 shown in FigureS9_RIMS4.tif</li> <li>RUFY3 shown in FigureS9_RUFY3.tif</li> <li>SH3BP4 shown in FigureS9_SH3BP4.tif</li> <li>SNX3 shown in FigureS9_SNX3.tif</li> <li>TDRKH shown in FigureS9_TDRKH.tif</li> <li>TOMM70 shown in FigureS9_TOMM70.tif</li> </ul>   Figure S10A: Immunoblots for validation of Parkin-dependent substrates in PINK1 WT and KO neurons. Scans of X-ray film: <ul> <li>CISD1 shown in Figure S10A _CISD1.tif (Halo-multiDSK pull-down) <ul> <li>  Figure S10A _INPUT_CISD1.tif (INPUT)</li> </ul> </li> <li>CPT1α shown in Figure S10A _ CPT1A.tif (Halo-multiDSK pull-down) <ul> <li>  Figure S10A _INPUT_ CPT1A.tif (INPUT)</li> </ul> </li> <li>Ubiquitin shown in Figure1C_ S10A _Ubiquitin.tif (same blot used for FigureS1C, Halo-multiDSK pull-down) <ul> <li>  Figure S10A _INPUT_Ubiquitin.tif (INPUT)</li> </ul> </li> <li>Phospho-Ser65 Ubiquitin shown in Figure1C_ S10A_UbiquitinPSer65.tif (same blot used for FigureS1C, Halo-multiDSK pull-down)</li> <li>GAPDH Shown in Figure S10A _INPUT_GAPDH.tif (INPUT)</li> </ul>   Figure S10B: Immunoblots for time-course of Parkin-dependent substrates in SH-SY5Y cells. Scans of X-ray film: <ul> <li>CISD1 shown in Figure S10B _CISD1.tif (Halo-multiDSK pull-down)</li> <li>CPT1α shown in Figure S10B _ CPT1A.tif (Halo-multiDSK pull-down)</li> <li>Ubiquitin shown in Figure S10B _Ubiquitin.tif (Halo-multiDSK pull-down)</li> <li>Phospho-Ser65 Ubiquitin shown in Figure S10B _UbiquitinPSer65.tif (Halo-multiDSK pull-down)</li> <li>GAPDH Shown in Figure S10B _GAPDH.tif </li> <li>Parkin shown in FigureS10B_Parkin.tif</li> <li>Phospho-Ser65 Parkin shown in FigureS10B_ParkinPSer65.tif</li> <li>PINK1 shown in FigureS10B_PINK1.tif</li> <li>OPA1 shown in FigureS10B_OPA1.tif</li> </ul>   Figure S11: Immunoblots for in vitro reconstitution assay in HeLa cells. Scans of X-ray film: <ul> <li>CISD1shown in FigureS11_CISD1.tif</li> <li>CPT1α shown in FigureS11_ CPT1a.tif</li> <li>CYB5B shown in FigureS11_CYB5B.tif</li> <li>HK1 shown in FigureS11_HK1.tif</li> <li>MFN2 shown in FigureS11_MFN2.tif</li> <li>VDAC shown in FigureS11_VDAC.tif</li> <li>Phospho-Ser65 Parkin shown in FigureS11_ParkinPSer65.tif</li> <li>Parkin shown in FigureS11_Parkin.tif</li> <li>Ubiquitin shown in FigureS11_Ubiquitin .tif</li> <li>Phospho-Ser65 Ubiquitin shown in FigureS11_UbiquitinPSer65 .tif</li> </ul>   FigureS12A: Purification of CPT1α protein. <ul> <li>FigureS12A_ Akta_Purifier_Curves.txt.</li> </ul>  Tab delimited text of data from the AKTA system, plotted in figure S12A. Columns are laid out as volume/measured parameter    for each parameter from the Akta. The columns plotted in figure S12A were mAU (columns A and B in excel) and fractions (columns M and N in excel). <ul> <li>Figure_S12A_Coomassie_Gel.tif. </li> </ul> Coomassie stained gels of fractions from the AKTA system in figure S12A. Top: Left hand Coomassie gel, figure S12A Bottom: Right hand Coomassie gel, figure S12A   FigureS12B-C: Purification of recombinant Parkin targets  Blots shown in the paper are highlighted in bold. <ul> <li>FigureS12B_Ub_Targets_High_800nm.tif</li> </ul> Coomassie stained gels of the parkin targets alongside a BSA curve. Left: Lane: 1: Mwt Marker, 2: Fam213A, 3: MAO-B, 4: CAMK2α, 5: MAO-A (figure S12B), 6: GST-MAPRE2, 7: MBP-CYB5R3, 8: MBP-CYB5B, 9: MBP-CYB5B, 10: SNX3, 11: His-SUMO-MAO-B, 12: GST-MAO-B, 13: 0.03125 ug BSA, 14: 0.0625 ug BSA, 15: 0.125 ug BSA, 16: 0.25 ug BSA, 17: 0.5 ug BSA, 18: 1 ug BSA Right: Lane: 1: Mwt Marker, 2: GST-MAO-A, 3: GST-FAM213A, 4: MBP-CAMK2α (figure S12B), 5: GST-TDRKH, 6: MBP-CPT1α, 7: MBP-CYB5B, 8: CAMK2β, 9:, 10: His-SUMO-MAO-B, 11: GST-MAO-B, 12: 0.03125 ug BSA, 13: 0.0625 ug BSA, 14: 0.125 ug BSA, 15: 0.25 ug BSA, 16: 0.5 ug BSA, 17: 1 ug BSA   <ul> <li>Figure_S12B_Ub_Targets_Low_800nm.tif</li> </ul> Coomassie stained gels of the parkin targets alongside a BSA curve. Lane: 1: Mwt Marker, 2: GST-Miro1 (figure S12B), 3: His-SUMO-Fam213A, 4: His-SUMO-MAPRE2, 5: His-SUMO-MAO-A, 6: His-SUMO-MAO-B, 7: His-SUMO-SNX26, 8: His-SNX3, 9: His-MAPRE2, 10: GST-FAM213A, 11: His-SUMO-CAMK2α, 12: His-SUMO-CAMK2β, 13: GST-FAM213A, 14: 0.03125 ug BSA, 15: 0.0625 ug BSA, 16: 0.125 ug BSA, 17: 0.25 ug BSA, 18: 0.5 ug BSA, 19: 1 ug BSA   <ul> <li>Figure_S12C_Ub_Targets_800nm.tif</li> </ul> Coomassie stained gels of the parkin targets in duplicate alongside a BSA curve. Top: Lane: 1: Mwt Marker, 2+3: ACSL1 45-end (Figure S12C), 4+5: SNX3 (Figure S12C), 6+7: MFN1, 8+9: MFN2, 10+11: 0.125 ug BSA, 12+13: 0.25 ug BSA, 14+15: 0.5 ug BSA, 16+17: 1 ug BSA Middle: Lane: 1: Mwt Marker, 2+3: CAMK2α, 4+5: CAMK2β (Figure S12C), 6+7: MAO-A, 8+9: MAO-B (Figure S12C), 10+11: 0.125 ug BSA, 12+13: 0.25 ug BSA, 14+15: 0.5 ug BSA, 16+17: 1 ug BSA Bottom: Lane: 1: Mwt Marker, 2+3: SRCIN, 4+5: His-MAPRE2, 6+7: FAM213A (Figure S12C), 8+9: CISD1, 10+11: 0.125 ug BSA, 12+13: 0.25 ug BSA, 14+15: 0.5 ug BSA, 16+17: 1 ug BSA   FigureS13-S14: Immunoblots for validation of Parkin-dependent substrates in in vitro studies. Blots shown in the paper are highlighted in bold. Scans of X-ray film: <ul> <li>FigureS13A-B_S14C-D_a6His_Film.tif</li> </ul> Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1 Membranes from left to right: Top: 1: FAM213A (Figure S13B), 2:  MAO-B (Figure S13A), 3: MAPRE2, 4: CAMK2β (Figure S14D) Top: 1: CAMK2α (Figure S14C), 2: Miro1, 3: MAO-A (Figure S13A)   <ul> <li>FigureS13A-B_S14C-D_aFLAG_Film.tif</li> </ul> Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1 Membranes from left to right: Top: 1: FAM213A (Figure S13B), 2: MAPRE2, 3: MAO-A (Figure S13A) Top: 1: MAO-B (Figure S13A), 2: CAMK2α (Figure S14C), 3: CAMK2β (Figure S14D), 4: Miro1     <ul> <li>FigureS13A-B_S14C-D_aTarget_05m00s_Film.tif</li> </ul> Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1 Membranes from left to right: Top: 1: FAM213A, 2: MAPRE2, 3: MAO-A Top: 1: MAO-B, 2: CAMK2α (Figure S14C), 3: CAMK2β (Figure S14D), 4: Miro1     <ul> <li>FigureS13A-B_S14C-D_aTarget_06m00s_Film.tif</li> </ul> Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1 Membranes from left to right: Top: 1: FAM213A (Figure S13B), 2: MAPRE2, 3: MAO-A (Figure S13A) Top: 1: MAO-B (Figure S13A), 2: CAMK2α, 3: CAMK2β, 4: Miro1     FigureS13C: Immunoblots for validation of Parkin-dependent substrates in in vitro studies. Scans of Western blots (LI-COR): <ul> <li>ACSL1 shown in FigureS13C_ACSL1_800nm.tif</li> <li>Flag-Ub (ACSL1) shown in Figure_S13C_ACSL1_Flag_800nm.tif</li> <li>His (ACSL1) shown in Figure_S13C_ACSL1_His_800nm.tif</li> </ul>   FigureS14B: Immunoblots for validation of Parkin-dependent substrates in in vitro studies. Blots shown in the paper are highlighted in bold. Scans of Western blots (LI-COR): <ul> <li>SNX3 shown in FigureS14B_SNX3_800nm.tif</li> </ul> Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1 Membranes, left to right: SNX3 (Figure S14B), FAM213A, Miro1, Miro1 <ul> <li>Flag-Ub (SNX3) shown in Figure_S14B_Flag_SNX3_800nm.tif</li> </ul> Gel loading: lanes: 1+2: – PINK1, 3+4:WT PINK1, 5+6: KD PINK1 Membranes, left to right: Miro1, FAM213A, Miro1, SNX3 (Figure S14B) <ul> <li>His (SNX3) shown in Figure_S14B_His_SNX3_700nm.tif</li> </ul> Gel loading: lanes: 1+2: – PINK1, 3+4: WT PINK1, 5+6: KD PINK1 Membranes, left to right: CPT1α, MFN1, SNX3 (Figure S14B), FAM213A   FigureS1: Immunocytochemistry of mouse cortical neurons. <ul> <li>FigureS1_TILE_maximun intensity projection.tif</li> </ul> Original acquisition: MAP2 neuronal marker (green-488); GFAP astrocytic marker (red-594); Hoechst nuclear marker (blue 361/497).  <ul> <li>FigureS1MGB (RGB).tif</li> </ul> Modified from FigureS1_TILE_maximun intensity projection.tif: MAP2 (green); GFAP (magenta); Hoechst (blue).   Table for Fig1DE.xlsx Numerical data for the charts shown in Figure1D and Figure1E.   Table for FigS1A.xlsx       Numerical data for quantification of Map2 and GFAP in mouse cortical neurons shown in FigureS1A.   Table for FigS1B (Cortical neurons_DIA_Report).xlsx       Numerical data for DIA proteomic analysis of AO and DMSO treated cortical neurons shown in FigureS1B.   Table for FigS2B_C_Oxygraph.xlsx Numerical data for the charts shown in FigureS2B and FigureS2C.   Table for Fig3A.xlsx Numerical data for total protein abundance in PINK1 WT and KO shown in Figure3A.   Table for Fig3B.xlsx Numerical data of abundance of phosphorylated-Ser65 or Ser57  shown in Figure3B.

opencc-by-4.0Jul 2023View details →

Damaged mitochondria recruit the effector NEMO to activate NF-κB signaling

Failure to clear damaged mitochondria via mitophagy disrupts physiological function and may initiate damage signaling via inflammatory cascades, though how these pathways intersect remains unclear. We discovered that NF-κB essential regulator NEMO is recruited to damaged mitochondria in a Parkin-dependent manner in a time-course similar to recruitment of the structurally-related mitophagy adaptor, OPTN. Upon recruitment, NEMO partitions into phase-separated condensates distinct from OPTN, but colocalizing with p62/SQSTM1. NEMO recruitment in turn recruits the active catalytic IKK component phospho-IKKb, initiating NF-κB signaling and the upregulation of inflammatory cytokines. Consistent with a potential neuroinflammatory role, NEMO is recruited to mitochondria in primary astrocytes upon oxidative stress. These findings suggest that damaged, ubiquitinated mitochondria serve as an intracellular platform to initiate innate immune signaling, promoting the formation of activated IKK complexes sufficient to activate NF-kB signaling. We propose that mitophagy and NF-κB signaling are initiated as parallel pathways in response to mitochondrial stress.

opencc-zeroNov 2023View details →

Genotypes and geographic positions of 5797 European white oaks from 636 locations genotyped at 355 nuclear SNPs and 28 maternally inherited SNPs of the chloroplast and mitochondria

The data set is the result of genetic inventory on 5797 white oaks collected at 636 locations all over Europe. The oaks trees were assigned in forest inventories as Quercus robur L. (3342), Quercus petraea Matt. (2090), Quercus pubescens Willd. (170) or as unspecified Quercus. spp. (195). The sampling had a focus on central and east Europe as well as the Black Sea and Caucasus region. All individuals were genotyped at 355 nuclear SNPs and 28 maternally inherited SNPs of the chloroplast and mitochondria. The combination of the maternally inherited SNPs resulted in 26 different haplotypes. The genotype of each individual is one row in the csv-file "genotypes". The genotypes at the nuclear markers are diploid and represented by two columns per gene marker. The genetic information at the organelle genome is haploid. For each of these gene markers one column is used. Genotypes are coded by Arabic numbers. The meaning of the numbers is explained in the table "coding genotypes" in a second csv-file. Each Individual has a unique "Genotype_ID" and a "Thuenen_Sample_ID". The "Thuenen_Sample_ID" is a unique ID that serves to identify the sample in our depository at the Thuenen Institute of Forest Genetics. Each individual has data on the geographic origin given as "Longitude" and "Latitude" in decimal degrees. For each individual the putative oak species ("Putative species") as it has been assigned in the forest inventories is given. The numbers of the "Haplotype" represent the multilocus combination of the mitochondrial and chloroplast SNPs of that individual.

opencc-zeroMar 2024View details →

Drp1 controls Complex II assembly and skeletal muscle metabolism by Sdhaf2 action on mitochondria

The Dynamin-related GTPase, Drp1 (encoded by Dnm1l) plays a central role in mitochondrial fission and is requisite for numerous cellular processes however its role in muscle metabolism remains unclear. Herein, we show that among human tissues, the highest number of gene correlations with DNM1L are in skeletal muscle. Knockdown of Drp1 (Drp1-KD) promoted mitochondrial hyperfusion in the muscle of male mice. Reduced fatty acid oxidation and impaired insulin action along with increased muscle succinate was observed in Drp1-KD muscle. Muscle Drp1-KD reduced Complex II assembly and activity as a consequence of diminished mitochondrial translocation of succinate dehydrogenase assembly factor 2 (Sdhaf2). Restoration of Sdhaf2 normalized Complex II activity, lipid oxidation, and insulin action in Drp1-KD myocytes. Drp1 is critical in maintaining mitochondrial Complex II assembly, lipid oxidation, and insulin sensitivity, suggesting a mechanistic link between mitochondrial morphology and skeletal muscle metabolism, which is clinically relevant in combatting metabolic-related diseases.

opencc-by-4.0May 2022View details →

Spectral flow cytometric tracking of mitochondria transfer in adipose tissue by age and diet

Unmixed fcs files for the stromal vascular fraction of epididymal (eWAT), inguinal (iWAT), and brown adipose tissue (BAT) in mtD2F/+AdipoqCre+/- and different ages or diets.  For a breakdown of samples and code, please see: <a href="https://github.com/ncborcherding/mtD2">https://github.com/ncborcherding/mtD2</a>. The full github repository at time of manuscript acceptance is also in the Zenodo repository under mtD2_GitHubRepo.zip. Antibody Panel:  <table align="center"> <tbody> <tr> <td> Laser </td> <td> Channel </td> <td> Antigen </td> <td> Fluorophore </td> <td> Final Dilution Factor </td> <td> Vendor </td> <td> Clone </td> <td> Catalog Number </td> </tr> <tr> <td> Violet </td> <td> V1 </td> <td> SiglecF </td> <td> BV 421 </td> <td> 1:400 </td> <td> BD Biosciences </td> <td> E50-2440 </td> <td> 562681 </td> </tr> <tr> <td> </td> <td> V2 </td> <td> CD44 </td> <td> Super Bright 436 </td> <td> 1:300 </td> <td> eBioscience </td> <td> IM7 </td> <td> 62-044-180 </td> </tr> <tr> <td> </td> <td> V3 </td> <td> CD11b </td> <td> Pacific Blue </td> <td> 1:300 </td> <td> BioLegend </td> <td> M1/70 </td> <td> 101224 </td> </tr> <tr> <td> </td> <td> V5 </td> <td> IL-33R⍺-biotin </td> <td> n/a </td> <td> 1:300 </td> <td> BioLegend </td> <td> DIH9 </td> <td> 145308 </td> </tr> <tr> <td> </td> <td> V5 </td> <td> Streptavidin (2°) </td> <td> BV 480 </td> <td> 1:300 </td> <td> BD Horizon </td> <td> n/a </td> <td> 564876 </td> </tr> <tr> <td> </td> <td> V7 </td> <td> MHC-II (I-A/I-E) </td> <td> BV 510 </td> <td> 1:300 </td> <td> BioLegend </td> <td> M5/114.15.2 </td> <td> 107636 </td> </tr> <tr> <td> </td> <td> V8 </td> <td> Ly6C </td> <td> BV 570 </td> <td> 1:400 </td> <td> BioLegend </td> <td> HK1.4 </td> <td> 128030 </td> </tr> <tr> <td> </td> <td> V10 </td> <td> KLRG1 </td> <td> BV 605 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 2F1/KLRG1 </td> <td> 138419 </td> </tr> <tr> <td> </td> <td> V11 </td> <td> F4/80 </td> <td> BV 650 </td> <td> 1:300 </td> <td> BioLegend </td> <td> BM8 </td> <td> 123149 </td> </tr> <tr> <td> </td> <td> V13 </td> <td> Ly6G </td> <td> BV 711 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 1A8 </td> <td> 127643 </td> </tr> <tr> <td> </td> <td> V14 </td> <td> CD3 </td> <td> BV 750 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 17A2 </td> <td> 100249 </td> </tr> <tr> <td> </td> <td> V15 </td> <td> CD206 </td> <td> BV 785 </td> <td> 1:200 </td> <td> BioLegend </td> <td> C068C2 </td> <td> 141729 </td> </tr> <tr> <td> Blue </td> <td> B1 </td> <td> mtDendra2 </td> <td> n/a </td> <td> n/a </td> <td> n/a </td> <td> n/a </td> <td> n/a </td> </tr> <tr> <td> </td> <td> B2 </td> <td> TCRb </td> <td> AF 488 </td> <td> 1:300 </td> <td> BioLegend </td> <td> H57-597 </td> <td> 109215 </td> </tr> <tr> <td> </td> <td> B3 </td> <td> CD8 </td> <td> Spark Blue 550 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 53-6.7 </td> <td> 100780 </td> </tr> <tr> <td> </td> <td> B8 </td> <td> CD45 </td> <td> PerCP </td> <td> 1:200 </td> <td> BioLegend </td> <td> 30-F11 </td> <td> 103130 </td> </tr> <tr> <td> </td> <td> B9 </td> <td> CD71 </td> <td> PerCP-Cy5.5 </td> <td> 1:200 </td> <td> BioLegend </td> <td> RI7217 </td> <td> 113816 </td> </tr> <tr> <td> </td> <td> B10 </td> <td> NKp46 </td> <td> PerCP-eFluor710 </td> <td> 1:200 </td> <td> eBioscience </td> <td> 29A1.4 </td> <td> 46-3351-82 </td> </tr> <tr> <td> Green </td> <td> YG1 </td> <td> TCRgd </td> <td> PE </td> <td> 1:300 </td> <td> BioLegend </td> <td> GL3 </td> <td> 118108 </td> </tr> <tr> <td> </td> <td> YG3 </td> <td> CD64 </td> <td> PE/Dazzle 594 </td> <td> 1:300 </td> <td> BioLegend </td> <td> X54-5/7.1 </td> <td> 139320 </td> </tr> <tr> <td> </td> <td> YG4 </td> <td> CD4 </td> <td> PE/Fire 640 </td> <td> 1:300 </td> <td> BioLegend </td> <td> GK1.5 </td> <td> 100482 </td> </tr> <tr> <td> </td> <td> YG5 </td> <td> CD62L </td> <td> PE-Cyanine5 </td> <td> 1:300 </td> <td> BioLegend </td> <td> MEL-14 </td> <td> 104410 </td> </tr> <tr> <td> </td> <td> YG7 </td> <td> CD11c </td> <td> PE-Cyanine5.5 </td> <td> 1:300 </td> <td> eBioscience </td> <td> N418 </td> <td> 35-0114-82 </td> </tr> <tr> <td> </td> <td> YG9 </td> <td> CD25 </td> <td> PE/Cyanine7 </td> <td> 1:200 </td> <td> BioLegend </td> <td> PC61 </td> <td> 102016 </td> </tr> <tr> <td> Red </td> <td> R1 </td> <td> CD9 </td> <td> APC </td> <td> 1:300 </td> <td> BioLegend </td> <td> MZ3 </td> <td> 124812 </td> </tr> <tr> <td> </td> <td> R2 </td> <td> CD127 </td> <td> AF 647 </td> <td> 1:200 </td> <td> BioLegend </td> <td> A7R34 </td> <td> 135020 </td> </tr> <tr> <td> </td> <td> R3 </td> <td> CD19 </td> <td> Spark NIR 550 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 6D5 </td> <td> 115568 </td> </tr> <tr> <td> </td> <td> R4 </td> <td> CD90.2 </td> <td> AF 700 </td> <td> 1:300 </td> <td> BioLegend </td> <td> 30-H12 </td> <td> 105320 </td> </tr> <tr> <td> </td> <td> R5 </td> <td> CD5 </td> <td> BUV 737 </td> <td> 1:300 </td> <td> BD Horizon </td> <td> 53-7-3 </td> <td> 612809 </td> </tr> <tr> <td> </td> <td> R6 </td> <td> Viability dye </td> <td> Zombie NIR </td> <td> 1:1,000 </td> <td> BioLegend </td> <td> n/a </td> <td> 423106 </td> </tr> <tr> <td> </td> <td> R7 </td> <td> NK1.1 </td> <td> APC/Fire 750 </td> <td> 1:300 </td> <td> BioLegend </td> <td> PK136 </td> <td> 108752 </td> </tr> <tr> <td> </td> <td> R8 </td> <td> B220 </td> <td> APC/Fire 810 </td> <td> 1:300 </td> <td> BioLegend </td> <td> RA3-6B2 </td> <td> 103278 </td> </tr> </tbody> </table>

opencc-by-4.0Oct 2022View details →

Mitochondria morphology quantification datasheet of different MIGA2 expressing cells

Mitochondria morphology quantification datasheet of different MIGA2 constructs expressing Hela cells: WT, MIGA2 KO, MIGA2 KO cells transfected with WT MIGA2, MIGA2 KO cells transfected with MIGA2 mutants (M1, M2, M3).

opencc-by-sa-4.0Jun 2024View details →

Data files for "Mitochondria-enriched protrusions are associated with brain and intestinal stem cells in Drosophila"

This entry is for our report "Mitochondria-enriched protrusions are associated with brain and intestinal stem cells in Drosophila" by Sharyn A. Endow, Sara E. Miller & Phuong Thao Ly in Commun Biol 2, 427  (2019). <a href="https://doi.org/10.1038/s42003-019-0671-4">https://doi.org/10.1038/s42003-019-0671-4</a> The deposited datasets contain the 1) EM raw images, 2) immunofluorescence microscopy (IFM) raw images, 3) live imaging raw sequences, and 4) data analysis files.

opencc-by-4.0Dec 2024View details →

Altered mitochondria-associated ER membranes (MAM) function shifts mitochondrial metabolism in amyotrophic lateral sclerosis (ALS)

Mitochondrial function is modulated by its functional interaction with the endoplasmic reticulum. Recent research indicates that these contacts are disrupted in familial models of amyotrophic lateral sclerosis. We report here this impairment in the crosstalk between mitochondria and the endoplasmic reticulum impedes the use of glucose-derived pyruvate as mitochondrial fuel, causing a shift to fatty acids to sustain energy production. Over time, this deficiency alters mitochondrial electron flow and the active/dormant status of complex I in spinal cord tissues, but not in the brain. These findings suggest MAM plays a crucial role in regulating cellular glucose metabolism and that its dysfunction may underlie the bioenergetic deficits observed in ALS. The dataset includes the lipid profiles of the total homogenates and motchondrial fractions from SOD1.

opencc-by-4.0Oct 2024View details →

BioTISR: Mitochondria (3D WF)

This dataset is part of BioTISR dataset. BioTISR is a biological image dataset for super-resolution microscopy, currently including 2D and 3D time-lapse image pairs of low-and-high resolution images of a variety of biology structures, aiming to provide a high-quality dataset of time-lapse biological SR images for the community to spark more developments of computational SR methods. At present, 2D dataset includes five specimens (clathrin-coated pits, lysosomes, outer mitochondrial membrane, microtubules, and F-actin) acquired with the GI/TIRF-SIM mode and nonlinear SIM mode of our Multi-SIM system, and 3D data includes three specimens (outer mitochondrial membrane, microtubules, and F-actin) acquired with 3D-SIM mode of the Multi-SIM system. For each type of specimen and each imaging modality, we acquired the raw data from at least 50 distinct regions-of-interest (ROI). For each ROI, we acquired two (3D data) or three (2D data) groups of N-phase × M-orientation × T-timepoint raw images with a constant exposure time but increasing the excitation light intensity, where (N, M, T) are (3, 3, 20) for TIRF-SIM and GI-SIM, (5, 5, 10) for nonlinear SIM, and (3, 5, 10) for 3D-SIM. The BioTISR dataset is related to the following paper:<a href="https://doi.org/10.1101/2024.05.04.592503">Chang Qiao, Shuran Liu, Yuwang Wang, Wencong Xu, et al. "Time-lapse Image Super-resolution Neural Network with Reliable Confidence Evaluation for Optical Microscopy." bioRxiv 2024.05.04.592503 (2024)</a>, which is an extension of our previously published <a href="https://doi.org/10.6084/m9.figshare.13264793.v9">BioSR dataset</a> (https://www.nature.com/articles/s41592-020-01048-5).

opencc-by-4.0Oct 2024View details →

BioTISR: Mitochondria (3D)

3D Mitochondria data of BioTISR dataset. BioTISR is a biological image dataset for super-resolution microscopy, currently including 2D and 3D time-lapse image pairs of low-and-high resolution images of a variety of biology structures, aiming to provide a high-quality dataset of time-lapse biological SR images for the community to spark more developments of computational SR methods. At present, 2D dataset includes five specimens (clathrin-coated pits, lysosomes, outer mitochondrial membrane, microtubules, and F-actin) acquired with the GI/TIRF-SIM mode and nonlinear SIM mode of our Multi-SIM system, and 3D data includes three specimens (outer mitochondrial membrane, microtubules, and F-actin) acquired with 3D-SIM mode of the Multi-SIM system. For each type of specimen and each imaging modality, we acquired the raw data from at least 50 distinct regions-of-interest (ROI). For each ROI, we acquired two (3D data) or three (2D data) groups of N-phase × M-orientation × T-timepoint raw images with a constant exposure time but increasing the excitation light intensity, where (N, M, T) are (3, 3, 20) for TIRF-SIM and GI-SIM, (5, 5, 10) for nonlinear SIM, and (3, 5, 10) for 3D-SIM. The BioTISR dataset is related to the following paper:<a href="https://doi.org/10.1101/2024.05.04.592503">Chang Qiao, Shuran Liu, Yuwang Wang, Wencong Xu, et al. "Time-lapse Image Super-resolution Neural Network with Reliable Confidence Evaluation for Optical Microscopy." bioRxiv 2024.05.04.592503 (2024)</a>, which is an extension of our previously published <a href="https://doi.org/10.6084/m9.figshare.13264793.v9">BioSR dataset</a> (https://www.nature.com/articles/s41592-020-01048-5).

opencc-by-4.0Mar 2023View details →

Molecular Models obtained in the paper "In situ architecture of the ER-mitochondria encounter structure"

The zip file contains prototypical NAMD input files to carry out the molecular dynamics flexible fitting simulations performed in the paper, and the initial and final structures (best models) obtained using the MDFF protocol.

opencc-zeroMay 2023View details →

Parkin regulates amino acid homeostasis at mitochondria-lysosome (M/L) contact sites in Parkinson's disease

Mutations in the E3 ubiquitin ligase parkin are the most common cause of early-onset Parkinson's disease (PD). Although parkin modulates mitochondrial and endolysosomal homeostasis during cellular stress, whether parkin regulates mitochondrial and lysosomal crosstalk under physiologic conditions remains unresolved. Using transcriptomics, metabolomics, and super-resolution microscopy, we identify amino acid metabolism as a disrupted pathway in iPSC-derived dopaminergic neurons from parkin PD patients. Compared to isogenic controls, parkin mutant neurons exhibit decreased mitochondria-lysosome contacts via destabilization of active Rab7. Subcellular metabolomics in parkin mutant neurons reveals amino acid accumulation in lysosomes and their deficiency in mitochondria. Knockdown of the Rab7 GTPase-activating protein TBC1D15 restores mitochondria-lysosome tethering and ameliorates cellular and subcellular amino acid profiles in parkin mutant neurons. Our data thus uncover a function of parkin in promoting mitochondrial and lysosomal amino acid homeostasis through stabilization of mitochondria-lysosome contacts and suggest that modulation of inter-organelle contacts may serve as a potential target for ameliorating amino acid dyshomeostasis in disease.

opencc-by-4.0Dec 2022View details →

Lipidomics of mitochondria isolated from human fibroblasts

Mitochondrial trifunctional protein (TFP) has a monolysocardiolipin-acyltransferase (MLCL-AT) activity and therefore establishes a link between fatty acid oxidation and cardiolipin remodelling. We hypothesized that TFP deficiency produced changes in cardiolipin and other phospholipid content and composition in mitochondria of TFP cultured fibroblasts. The data showed that phospholipid profiles varied among patient fibroblasts. There was a correlation between genotype and the phospholipid profiles. Two profiles were found when cardiolipin, monolysocardiolipin, and oxidized cardiolipin and other phospholipids were considered, one of them similar to Barth syndrome. We concluded that cardiolipin remodeling may play a role in the pathogenesis of at least some patients with TFP/LCHAD deficiency. A previously described protocol was employed for the identification and quantification of mitochondrial phospholipids (including CL) and oxidized phospholipids by LC-MS/MS [1]. Briefly, lipids were extracted using the Folch method, total phosphate content was quantified, and samples were then analyzed using a LC-MS/MS system. The identification and quantification of the lipid species were achieved with an optimized workflow using SIEVE 2.2 software, and an in-house database. [1]. Chao H, Anthonymuthu TS, Kenny EM, Amoscato AA, Cole LK, Hatch GM et al. Disentangling oxidation/hydrolysis reactions of brain mitochondrial cardiolipins in pathogenesis of traumatic injury. JCI Insight 2018;3(21).

opencc-zeroJul 2023View details →

Data for: A unique C-terminal domain contributes to the molecular function of restorer-of-fertility proteins in plant mitochondria

Restorer-of-fertility (Rf) genes have practical applications in hybrid seed production as a means to control self-pollination. They encode pentatricopeptide repeat (PPR) proteins that are targeted to mitochondria where they specifically bind to transcripts that induce cytoplasmic male sterility and repress their expression. We have identified a unique domain, RfCTD (Restorer-of-fertility C-terminal domain), which discriminates Restorer-of-fertility-like (RFL) proteins from hundreds of PPR proteins encoded in plant genomes. Using the sequence of this domain from hundreds of plant species, we have constructed a sequence profile that can quickly and accurately identify RfCTD sequences in plant genomes or transcriptomes. This data set contains PPR genes identified in 213 plant genomes (as summarised in accompanying table).

ClinicalTrials.gov36/100

Rosiglitazone Effect on Mitochondria and Lipoatrophy

ClinicalTrials.gov study NCT00367744. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →