Dr.ManuelJ.Muñoz
BiologíaCelular:Mitocondria
Caracterís)casgenerales:1.OrganelacondoblemembranaysupropioADN(mtDNA).
2. Involucrada en la generación de ATP (cadena de transporteelectrónico/fosforilaciónoxidaBva).3. Regulador central del metabolismo celular (ciclo de Krebs, síntesis ybeta oxidación de ácidos grasos, síntesis de nucleóBdos, aminoácidos,hemo).4.ModuladordesBnocelular:-apoptosis
-señalretrógrada
Fallasensufuncionamientoseasocianadiversaspatologías
Origen:endosimbiosisdeunabacteriaheterotrófaconsumidoradeO2
AP Biology 2005-2006
Endosymbiosis theoryEvolution of eukaryotes
DNAMitocondrial(mtDNA)enmamíferos
- Herencia materna, duplicaciónindependiente del DNA nucelar,presenteenmúl7plescopias.
- De 1 a 8 genomas se asocian conproteínasformando“nucleoides”.
- Codificapara2rRNAs,22tRNAs&13mRNA (todos factores respiración/OXPHOS).
- Una única region de control de laduplicación y expresión del mtDNA(D-loop).
- Hebra H: Un promotor para losrRNAs , ot ro para 12 mRNAs(policistrón). Procesamiento porcorteygeneraciondetRNAs.
mtDNAhumano:16.6kpb
Figure 1 | Mitochondrial DNA and the human respiratory chain/oxidative phosphorylation system. a | The human mitochondrial genome. The 37 mitochondrial DNA (mtDNA)- encoded genes include seven subunits of complex I (ND1, 2, 3, 4, 4L, 5 and 6), one subunit of complex III (cytochrome b (Cyt b)), three subunits of complex IV (Cyt c oxidase (COX) I, II and III), two subunits of complex V (A6 and A8), two rRNAs (12S and 16S) and 22 tRNAs (one-letter code). Also shown are the origins of replication of the heavy strand (O
H) and the
light strand (OL), and the promoters of transcription of
the heavy strand (HSP) and light strand (LSP). b | Oxidative phosphorylation (OXPHOS) complexes. The system is comprised of complex I (NADH dehydrogenase-CoQ reductase), complex II (succinate dehydrogenase), complex III (ubiquinone-Cyt c oxidoreductase), complex IV (COX) and complex V (ATP synthase; F
0 and F
1 denote the
proton-transporting and catalytic subcomplexes, respectively), plus two electron carriers, coenzyme Q (CoQ; also called ubiquinone) and Cyt c. Complexes I–IV pump NADH- and FADH
2-derived protons (produced by the
tricarboxylic acid (TCA) cycle and the β-oxidation ‘spiral’) from the matrix across the mitochondrial inner membrane to the intermembrane space to generate a proton gradient while at the same time transferring electrons to molecular oxygen to produce water. The proton gradient, which makes up most of the mitochondrial transmembrane potential (Δψ
m), is used to do work by being dissipated
across the inner membrane in the opposite direction through the fifth complex (ATP synthase), thereby generating ATP from ADP and free phosphate. Complexes I, III, IV and V contain both mtDNA- and nuclear DNA (nDNA)-encoded subunits, whereas complex II, which is also part of the TCA cycle, has only nDNA-encoded subunits. Note that CoQ also receives electrons from dihydroorotate dehydrogenase (DHOD), an enzyme of pyrimidine synthesis. Polypeptides encoded by nDNA are in blue (except for DHOD, which is in pink); those encoded by mtDNA are in colours corresponding to the colours of the polypeptide-coding genes (shown in a). The ‘assembly’ proteins are all nDNA-encoded. IMS, intermembrane space; MIM, mitochondrial inner membrane. Part a is modified from REF. 149 © (2006) Wiley.
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Nature Reviews | Genetics
COX I
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Complex IPolypeptides Complex IIIComplex II Complex VComplex IV
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subunits10 23
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subunits104 ~1410
~11Assembly
proteins~9~2 ~3~30
Succinate Fumarate
O2
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e–
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Figure 1 | Mitochondrial DNA and the human respiratory chain/oxidative phosphorylation system. a | The human mitochondrial genome. The 37 mitochondrial DNA (mtDNA)- encoded genes include seven subunits of complex I (ND1, 2, 3, 4, 4L, 5 and 6), one subunit of complex III (cytochrome b (Cyt b)), three subunits of complex IV (Cyt c oxidase (COX) I, II and III), two subunits of complex V (A6 and A8), two rRNAs (12S and 16S) and 22 tRNAs (one-letter code). Also shown are the origins of replication of the heavy strand (O
H) and the
light strand (OL), and the promoters of transcription of
the heavy strand (HSP) and light strand (LSP). b | Oxidative phosphorylation (OXPHOS) complexes. The system is comprised of complex I (NADH dehydrogenase-CoQ reductase), complex II (succinate dehydrogenase), complex III (ubiquinone-Cyt c oxidoreductase), complex IV (COX) and complex V (ATP synthase; F
0 and F
1 denote the
proton-transporting and catalytic subcomplexes, respectively), plus two electron carriers, coenzyme Q (CoQ; also called ubiquinone) and Cyt c. Complexes I–IV pump NADH- and FADH
2-derived protons (produced by the
tricarboxylic acid (TCA) cycle and the β-oxidation ‘spiral’) from the matrix across the mitochondrial inner membrane to the intermembrane space to generate a proton gradient while at the same time transferring electrons to molecular oxygen to produce water. The proton gradient, which makes up most of the mitochondrial transmembrane potential (Δψ
m), is used to do work by being dissipated
across the inner membrane in the opposite direction through the fifth complex (ATP synthase), thereby generating ATP from ADP and free phosphate. Complexes I, III, IV and V contain both mtDNA- and nuclear DNA (nDNA)-encoded subunits, whereas complex II, which is also part of the TCA cycle, has only nDNA-encoded subunits. Note that CoQ also receives electrons from dihydroorotate dehydrogenase (DHOD), an enzyme of pyrimidine synthesis. Polypeptides encoded by nDNA are in blue (except for DHOD, which is in pink); those encoded by mtDNA are in colours corresponding to the colours of the polypeptide-coding genes (shown in a). The ‘assembly’ proteins are all nDNA-encoded. IMS, intermembrane space; MIM, mitochondrial inner membrane. Part a is modified from REF. 149 © (2006) Wiley.
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COX I
COX II
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CoQ
Cyt c
ND4
ND3
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ND4L
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DHOD
Complex IPolypeptides Complex IIIComplex II Complex VComplex IV
7mtDNA-encoded
subunits10 23
~39nDNA-encoded
subunits104 ~1410
~11Assembly
proteins~9~2 ~3~30
Succinate Fumarate
O2
2H2O
e–
e–
e– e–
e–e–
ADP ATP
F1
F0
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b
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REVIEWS
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GeneracióndeATP(OXPHOS):“Thepowerhouseofthecell”
be preferentially found among the full-length unimportedand unprocessed species detected on the organellar surface?Such molecules were clearly not cotranslationally imported.Perhaps these proteins, as a group, tend to rapidly adoptfolded conformations that inhibit translocation. In this case,localized synthesis could be an adaptation that alleviatesthis problem, albeit incompletely, by facilitating cotransla-tional import of a significant fraction of molecules. It iscurrently unknown whether the fully synthesized precursorproteins bound to the mitochondrial outer surface aredestined to be imported or degraded (Zahedi et al. 2006).The observation that unprocessed pre-Atp2 accumulates toan abnormally high level outside of mitochondria in cellstranslating a chimeric ATP2 mRNA lacking the localizationsignal in its 39-UTR (Margeot et al. 2002) is consistent withthe possibility that post-translational import of pre-ATP2 isinefficient in vivo, despite the fact that it occurs in vitro(Maccecchini et al. 1979). Perhaps the pre-ATP2 moleculesdetected on the surface of mitochondria were actually trans-lated from those ATP2 mRNA molecules, !50% of the total,that were not localized to mitochondria-bound polysomes.In any event, it is clear that localized synthesis of pre-ATP2somehow facilitates its import.
Another fascinating but imperfect correlation emergedfrom the ranking of mRNAs by their propensity to bemitochondrially localized. Those mRNAs found most selec-tively in mitochondria-bound polysomes tend to encodeproteins whose evolutionary origins can be clearly traced toBacteria and/or Archaea. Conversely, those mRNAs foundmost selectively in free polysomes tend to encode proteinslacking clear homologs in those phylogenetic domains andare therefore likely to be more recently evolved inventions
of Eukarya (Marc et al. 2002; Garcia et al. 2007a). This corre-lation runs in parallel with the observation that the more locallysynthesized proteins tend to be either ancient, conserved com-ponents of the mitochondrial genetic system or respiratory com-plexes or conserved proteins with roles in assembly of thosecore components (Margeot et al. 2005). In the case of cyto-chrome c oxidase, for example, proteins with bacterial orthologsthat assemble mitochondrially coded core subunits in the innermembrane, insert metal cofactors, and synthesize the specificheme A cofactor are all selectively translated at the mitochon-drial surface (although they do not all have clear bacterial orarchaeal ancestors). In contrast, the eukaryotic-specific subunitsof cytochrome c oxidase that surround the catalytic core of theenzyme are all selectively translated on free polysomes.
There are no obvious structural or chemical similaritiesamong the set of proteins most selectively synthesized at themitochondrial surface (Marc et al. 2002). So, what selectiveconstraints maintain the localized translation of more an-ciently evolved proteins? It has been argued that synthesislocalized to mitochondria may promote efficient assembly ofcore components of complexes by directing import of pro-teins to specific regions (Margeot et al. 2005; Garcia et al.2007a). While this is an attractive hypothesis, there is nostrong evidence that localized synthesis of any mitochon-drial protein is spatially organized on the organellar surface.Nor is it obvious why nonlocalized translation of other es-sential but peripheral subunits of complexes would be ad-vantageous. Interestingly, the mitochondria-bound mRNAstend to be synthesized early during the yeast metabolic cycle(Tu et al. 2005; Lelandais et al. 2009).
Complex mechanisms for mRNA localization
Regardless of why some mRNAs are localized to mitochon-dria while others are not, the example of ATP2 demonstratesthe importance of mRNA targeting for mitochondrial bio-genesis. How are localized mRNAs brought and tetheredto the organelles? mRNA 39-UTRs contain information forlocalization in at least eight cases that have been experimen-tally examined (Corral-Debrinski et al. 2000; Marc et al.2002; Margeot et al. 2002).
One factor with apparent roles in localization of manymRNAs encoding mitochondrial proteins is Puf3, a member ofthe Pumilio-homology domain family (PUF) of RNA-bindingproteins. PUF family proteins are found in a wide variety ofeukaryotes and carry out a wide variety of functions throughtheir ability to mediate interactions between target RNAsand other proteins (Quenault et al. 2011). An initial surveyof yeast PUF protein functions (Olivas and Parker 2000)revealed that a puf3∆ mutation strongly affected theCOX17 mRNA, which encodes a (Eukarya-specific) mito-chondrial copper-binding protein required for cytochromec oxidase assembly (Glerum et al. 1996). The presence ofPuf3 was shown to stimulate deadenylation and degradationof COX17 mRNA, but did not affect its translation as mea-sured by the degree of polysome association or the level ofaccumulated Cox17 protein.
Figure 3 Cytoplasmic synthesis of some mitochondrial proteins is local-ized to the organelles, while the synthesis of others is not. The figuredepicts three examples: (1) The ATP2 mRNA is highly localized tomitochondria-bound polysomes (Garcia et al. 2007b), although factorsrequired for this localization are unknown. (2) The BCS1 mRNA is alsoselectively found in mitochondria-bound polysomes, and its localization ispartially dependent upon the mitochondrially localized RNA-binding pro-tein Puf3 and the Puf3-binding sites in its 39-UTR (Saint-Georges et al.2008). (3) The COX4 mRNA is exclusively found on free polysomes, un-associated with mitochondria (Garcia et al. 2007b). The Atp2, Bcs1, andCox4 proteins all traverse the outer membrane via the TOM complexpore.
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REVIEW ARTICLE | FOCUS NATURE CELL BIOLOGY
the factors that dictate the choice for pyruvate-flux between PC and PDC are little studied27,29,30. Therefore, these enzymes may have important functions beyond TCA-cycle-flux for bioenergetics.
Glutamine and branched-chain amino acids. Catabolism of glu-tamine, the most abundant amino acid in plasma, often starts in the mitochondria, and its carbon and nitrogen atoms are distributed into macromolecules (DNA, RNA, protein and lipids) and other metabolites, such as TCA cycle intermediates (important in bioen-ergetics), amino acids, nucleotides and glutathione31.
In mitochondria, glutaminase (GLS) converts glutamine into glu-tamate and ammonia. Either transaminase or glutamate dehydro-genase (GDH) converts glutamate into α -ketoglutarate (α -KG)32,33. Glutamine anaplerosis sustains TCA cycle intermediates in condi-tions of limited glucose and MPC inhibition, demonstrating the potential flexibility of these metabolic nodes34,35. Glutamine anaple-rosis is critical for meeting the energetic requirements of prolifera-tive cells, such as T cells during the transition from quiescent naïve T cells to effector cells, and in cancers, particularly those with MYC
elevation32,36,37. GLS inhibition suppresses proliferation, and GLS inhibitors are being evaluated in clinical studies for a number of cancers31,38,39. However, sensitivity to GLS inhibition in vitro is not always consistent in vivo, and is dependent on extracellular cystine levels40. This emphasizes the need for investigators to study the effect of the microenvironment on metabolic dependencies and to validate experiments in vivo.
Although glutamine transporters at the plasma membrane have been identified41, the mitochondrial glutamine transporter has not been fully characterized42,43. This critical area of research is chal-lenging to address because there are likely multiple mechanisms for glutamine import.
The branched-chain amino acids (BCAAs) leucine, isoleucine and valine are major sources of cellular energy through generation of acetyl CoA and succinyl CoA (ref. 44). The tissue of origin dictates dependency on BCAA catabolism in normal physiology and in can-cer45. In normal physiology, myocytes and adipocytes activate mito-chondrial BCAA catabolic enzymes to support ATP production during exercise or fasting, and during differentiation, respectively46,47.
Glucose
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Fig. 1 | Mitochondria are the powerhouse of the cell. Mitochondria integrate fuel metabolism to generate energy in the form of ATP. Mitochondria oxidize pyruvate (derived from glucose or lactate), fatty acids and amino acids to harness electrons onto the carriers NADH and FADH2. NADH and FADH2 transport these electrons to the electron transport chain, in which an electrochemical gradient is formed to facilitate ATP production through oxidative phosphorylation. VDAC, voltage-dependent anion channel; IDH2, isocitrate dehydrogenase 2; OGDH, α -ketoglutarate dehydrogenase; SDH, succinate dehydrogenase; BCAT2, branched-chain amino transferase 2; ACS, acyl CoA synthetase. Electrons and reducing equivalents are shown in yellow.
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CiclodeKrebs&Beta-oxidacióndeácidosgrasos
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BCAA catabolism is repressed in maple syrup urine disease, which is caused by mutations to branched-chain keto acid dehydroge-nase (BCKDH) and causes dysfunction of immune cells, skeletal muscle and the central nervous system48. Although mitochondrial BCAA catabolism is critical in these pathologies, it is unknown how BCAAs are imported into the mitochondria. Identifying their trans-port mechanisms will be critical to our understanding of mitochon-drial BCAA catabolism in cellular homeostasis.
Fatty acid oxidation. Palmitate, a 16-carbon fatty acid (FA), stores 39 KJ g–1 of energy, compared to 16 KJ g–1 stored in glucose49. Therefore, FAs are a major source of cellular energy, particularly under conditions of nutrient stress. Mitochondrial FA import is a rate-determining step for fatty acid oxidation (FAO) and demon-strates how metabolic compartmentalization adapts to cellular state. As long-chain FAs are unable to cross mitochondrial membranes, mitochondria have evolved an intricate set of reactions and trans-porter activities to allow fat to access mitochondrial β -oxidation
machinery. The outer mitochondrial membrane (OMM) enzyme carnitine palmitoyl transferase 1 (CPT1) forms acylcarnitines from fatty acyl CoAs50. Acylcarnitines are shuttled into mitochondria through the carnitine–acylcarnitine translocase (SLC25A20) in the IMM. CPT2 liberates FA from carnitine, initiating FAO51. Acetyl CoA from FAO is used for the TCA cycle as well as for aspartate and nucleotide synthesis52.
CPT1 activity is tightly controlled by a network of metabolites, linking it to cellular nutrient status. Malonyl CoA, generated by the enzyme acetyl CoA carboxylase (ACC), represses CPT1 to inhibit acylcarnitine import53. Malonyl CoA is the initiating metabolite for FA synthesis, and its levels dictate the balance of fat synthesis or oxidation within a cell. In low-energy conditions, AMP-activated protein kinase (AMPK) phosphorylates and inhibits ACC, decreas-ing malonyl CoA and increasing CPT1 activity54. ACC2 is also hydroxylated by the dioxygenase prolyl hydroxylase 3 (PHD3)55. Hydroxylation promotes ACC2 activity in nutrient abundance. These enzymes are altered in some cancers and human diseases as
Nucleotide synthesis Glucose and heme synthesis
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Fig. 2 | Mitochondria are biosynthetic hubs. The mitochondria are a critical source of building blocks for biosynthetic pathways, including nucleotide synthesis, fatty acid and cholesterol synthesis, amino acid synthesis, and glucose and heme synthesis. Compartmentalization is a key feature of biosynthetic pathways. While many of the enzymes listed are bi-directional, arrows highlight the biosynthetic functions. Enzymes are circled in grey and brown. FTDH, formate dehydrogenase; TA, transaminase; GC, glutamate carrier; FLVCR, feline leukaemia virus subgroup C receptor 1.
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Otrasfuncionesmetabólicas
LaMitocondriaesunaorganelaextremadamentedinámica
-Elnúmeroporcélulavariade1a1000.Representahasta1/5delvolumencelular.-Suformavaríadesdefilamentosahastaglobular.-Suubicaciónestáregulada:interacciónconcitoesquelto(kinesina/dineina).-Elmetabolismocontrolalafisiónyfusióndelasmembranasmitocondriales:
Mitochondrial Fission, Fusion,and StressRichard J. Youle1* and Alexander M. van der Bliek2*
Mitochondrial fission and fusion play critical roles in maintaining functional mitochondria when cellsexperience metabolic or environmental stresses. Fusion helps mitigate stress by mixing the contentsof partially damaged mitochondria as a form of complementation. Fission is needed to create newmitochondria, but it also contributes to quality control by enabling the removal of damaged mitochondriaand can facilitate apoptosis during high levels of cellular stress. Disruptions in these processes affectnormal development, and they have been implicated in neurodegenerative diseases, such as Parkinson’s.
Mitochondria are double-membrane–bound subcellular organelles that pro-vide a host of metabolic functions,
including energy production through oxidativephosphorylation. Mitochondrial morphologiesvary widely among different cell types. Fibro-blast mitochondria, for example, are usually longfilaments (1 to 10 mm in length with a fairlyconstant diameter of ~700 nm), whereas hepato-cyte mitochondria are more uniformly spheres orovoids. When mitochondria are viewed in livecells, it becomes immediately apparent that theirmorphologies are far from static. Their shapeschange continually through the combined actionsof fission, fusion, and motility. Rapid fission andfusion of mitochondria in cultured fibroblastsallows for the complete redistribution of mito-chondrial green fluorescent protein (GFP) fromone mitochondrion to all the other mitochon-dria of a cell within an hour. The wide range ofmitochondrial lengths observed in different celltypes and under different conditions results fromchanges in the balance between the rates of mito-chondrial fission and fusion. Here, we discusshow fission and fusion contribute to mitochon-drial quality control and the responses of mam-malian cells to stress.
Mitochondrial Fusion and Fission ProteinsMitochondrial fission and fusion processes areboth mediated by large guanosine triphosphatases(GTPases) in the dynamin family that are wellconserved between yeast, flies, and mammals(1). Their combined actions divide and fuse thetwo lipid bilayers that surround mitochondria.The mitochondrial inner membrane, which en-closes the matrix, is folded into cristae that con-tain membrane-bound oxidative phosphorylationenzyme complexes and the bulk of the solubleelectron transport proteins such as cytochrome c,whereas the smooth mitochondrial outer mem-
brane encapsulates the inner membrane and anintermembrane space.
Fission is mediated by a cytosolic dynaminfamily member (Drp1 in worms, flies, and mam-mals and Dnm1 in yeast). Drp1 is recruited fromthe cytosol to form spirals around mitochondriathat constrict to sever both inner and outer mem-branes. Yeast share with mammals this core func-tion of Drp1 but have distinct accessory proteins.Mdv1 recruits Dnm1 to mitochondrial fissionsites in yeast, whereas Mid49, Mid51, and Mffrecruit Drp1 to mitochondria in mammals (2),often at sites where mitochondria make contactwith the endoplasmic reticulum (3). Fusion be-tweenmitochondrial outermembranes ismediatedbymembrane-anchored dynamin family membersnamed Mfn1 and Mfn2 in mammals, whereasfusion between mitochondrial inner membranesis mediated by a single dynamin family membercalled Opa1 in mammals. Mitochondrial fissionand fusion machineries are regulated by proteol-ysis and posttranslational modifications (1).
Mitochondrial fission is essential for growingand dividing cells to populate themwith adequatenumbers of mitochondria. It has been less clearwhy mitochondrial fission and fusion are alsoneeded for nonproliferating cells, but the impor-tance of these processes is evident from non-proliferating neurons, which cannot survive
without mitochondrial fission, and from two hu-man diseases, dominant optic atrophy andCharcotMarie Tooth disease type 2A, which are causedby fusion defects. The importance of mitochon-drial fusion for embryogenesis was shown withMfn1 and Mfn2 knock-out mice, which die inutero at midgestation because of a placental defi-ciency, whereas the Mfn1 Mfn2 double knock-out mice die even earlier in development (4).Mouse embryo fibroblasts (MEFs) derived fromthe double knock-out mice do survive in culture,despite a complete absence of fusion, but some oftheir mitochondria display a reduced mitochon-drial DNA (mtDNA) copy number and lose mem-brane potential, causing problems with adenosinetriphosphate (ATP) synthesis (5). Mitochondrialfusion is therefore not absolutely essential for cellsurvival in vitro, but it is required for embryonicdevelopment and for cell survival at later stagesin development (4). These differential require-ments for fusion may stem from higher demandson oxidative metabolism in different cell types oron other functions that are indirectly affected byfusion, such asmitochondrial motility in neurons.
Fusion Promotes ComplementationBetween Damaged MitochondriaMitochondria have their own small circular ge-nomes, encoding select subunits of ATP synthesisand electron transport proteins that form oxida-tive phosphorylation complexes with other sub-units encoded by the nuclear genome, as well astransfer and ribosomal RNAs (tRNAs and rRNAs)needed for their translation. A single somatic cellcan have thousands of copies of these genomes,which are grouped in protein-rich complexes callednucleoids, with between one and eight genomecopies per nucleoid (6). Mutations and deletionsthat occasionally arise in mitochondrial DNAyield a heteroplasmic mixture of wild-type andmutant mitochondrial genomes within one cell.Maternal inheritance of these mutations can causemitochondrial diseases, such as mitochondrialencephalomyopathy with lactic acidosis andstrokelike episodes (MELAS) and myoclonus
REVIEW
1Biochemistry Section, Surgical Neurology Branch, National In-stitute of Neurological Disorders and Stroke, National Institutesof Health, Bethesda, MD 20892, USA. 2Department of BiologicalChemistry, David Geffen School of Medicine at University ofCalifornia–Los Angeles, Los Angeles, CA 90095, USA.*To whom correspondence should be addressed. E-mail: [email protected] (R.J.Y.); [email protected] (A.M.v.d.B.)
Damaged
Complementation of mitochondrial function by fusion
Fusion is stimulatedby energy demand
and stress
Fission generatesnew organellesand facilitatesquality control
Healthy
Fig. 1. Fusion rescues stress by allowing functional mitochondria (green) to complement dysfunctionalmitochondria (yellow) by diffusion and sharing of components between organelles. Stress-induced hyper-fusion yields maximal potential (light green), whereas under relaxed conditions cells are able to segregatethe damaged (yellow) ones.
31 AUGUST 2012 VOL 337 SCIENCE www.sciencemag.org1062
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substantia nigral neuron degeneration. PINK1-and Parkin-deficient Drosophila display muscleand neuron degeneration that is associated withswollen and defective mitochondria (25–27).Consistent with the model that mitochondrialfission and fusion promotes mitochondrial qual-ity control, inhibition of mitochondrial fusion orpromotion of mitochondrial fission compensatesfor deficiencies of PINK1 and Parkin in flies.Furthermore, Parkin overexpression in flies res-cues unfolded protein stress of mitochondriathrough autophagy (28), and stimulation ofautophagy rescues depolarized mitochondriaaccumulation in dopaminergic neurons fromParkin-deficient Drosophila (29).
Banish Mitochondria That TrulyAre UncoupledDefective mitochondria can be toxic by generatingexcessive amounts of ROS, by consuming ATPthrough reversal of ATP synthase, and by interferingwith a host of other metabolic processes (Box 1).Low levels of damage might be corrected by com-plementation through mitochondrial fusion, butbadly damagedmitochondriawill contaminate othermitochondria if they are allowed to rejoin themitochondrial network before their eliminationby autophagy. Several mechanisms are at work tostop this from happening. A first line of defenseis provided by a built-in requirement of the mito-chondrial inner membrane fusion machinery formembrane potential (30). Vertebrates have elabo-rated on thismechanism by providing a second lineof defense through proteolytic inactivation of theinner membrane fusion dynamin OPA1. Proteoly-sis is mediated by the mitochondrial inner mem-brane protease OMA1, which is rapidly activatedby low membrane potential and low levels of ATP(31, 32). The outer membranes of these mitochon-dria can still fuse, even without functional OPA1or membrane potential, but the inner membrane–bound matrix compartments do not fuse, resulting
in several matrix compartments surrounded by acommon outer membrane, like peas in a pod.
The last line of defense is provided by the Pink1and Parkin pathway through the ubiquitination ofthe mitochondrial outer membrane fusion proteinsMfn1 and Mfn2. Ubiquitination of these proteinsleads to their extraction from themembrane by p97and their degradation by proteasomes (16). In ad-dition, Pink1 and Parkin disrupt mitochondrial mo-tility by degrading the small GTPase Miro, whichserves as an adaptor for kinesin-dependent transport
and is also needed for mitochondrial fusion (33).Ultimately, uncoupled mitochondria lose both theirinner and outermembrane fusionmachineries, there-by preventing them from fusing with and poison-ing the healthy mitochondrial network. Purposefulsegregation and disposal of damaged mitochondriathrough changes in fission and fusion pathwaysare therefore integral parts of mitochondrial quality-control mechanisms.
Is Debris Also Sorted Inside Mitochondria?The gradual accumulation of damaged compo-nents poses a problem for the mitophagic dispo-sal process. If damaged components were evenlydistributed, then the simple act of fission throughDrp1 would not generate the asymmetry neededfor inducing mitophagy by selective loss of mem-brane potential. It seems that asymmetric sortingof debris would be needed to generate the dif-ferences in membrane potential between daughtermitochondria that have been observed immedi-ately after fission (19). Accumulation of damagedcomponents in a subset of daughter mitochondriawould enable their selective disposal, thus help-ing to rejuvenate the remaining population ofmitochondria (Fig. 2).
How might mitochondria achieve this type ofasymmetric fission? The mechanism is not yetknown, but it seems likely that damaged proteinsform aggregates within the mitochondrial matrix.Perhaps there is a way to stow these aggregates atthe tips of mitochondria, thus providing a startingpoint for polarized fission. A precedent for thiswas set by bacteria, which remove aggregates by
Fig. 3. PINK1 is constitutively de-graded by the inner mitochondri-al membrane protease PARL andmaintained at low levels on healthymitochondria. When a mitochon-drion becomes damaged to thepoint of depolarizing the mem-brane potential across the innermembrane, PINK1 import to theinner membrane is prevented,thereby sequestering it on theouter mitochondrial membraneand away from PARL. PINK1 accu-mulates there and recruits the E3ligase Parkin from the cytosol viaPINK1 kinase activity. Parkin con-jugates ubiquitin (Ub) to a varietyof proteins on the outer mitochon-drial membrane and mediates theproteosomal elimination of mito-fusins 1 and 2. Lastly, Parkin in-duces autophagic elimination ofthe dysfunctional mitochondria. Thispathway may constitute a quality-controlmechanismtoeliminatedam-aged mitochondria. UPS, ubiquitinproteasome system.
Model ofParkin-induced
mitophagy
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Fig. 2. Autophagy could purify the cellular pool of mitochondria if debris is aggregated and segregatedby fission in a subset of mitochondria. If deleterious components (black fibers) are asymmetricallydistributed or aggregated, fission could lead to cleansing of daughter mitochondrion (green) by pre-venting fusion and inducing mitophagy of the impaired ones (yellow).
31 AUGUST 2012 VOL 337 SCIENCE www.sciencemag.org1064
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content. We found that ∼50% of thetranscription genes fall above this thresh-old (Fig. 5C, inset). Genes belonging tothe “translation” family display similartrends (not shown), with ∼60% of themabove the threshold for protein doubl-ing. Notably, most of the genes codingfor ribosomal proteins are among thosewith larger differential expression be-tween High and Low populations (Fig.5D). Because ribosomal synthesis is tight-ly controlled (Lempiainen and Shore2009), our data suggest that cells in theHigh population have a greater numberof ribosomes than Low cells. We furthertested this possibility by costainingcells with CMXRos and YOYO-1 (an in-dicator of the amount of ribosomalRNA) (Calabuig et al. 2005). As expected,ribosomal RNA content scaled withmitochondrial mass (Supplemental Fig.S6A). In agreement with this evidence,we found from our RNA-seq data that ex-pression ofMYC, a regulator of ribosomalgene transcription initiation (van Rigge-len et al. 2010), was increased 3.4 timeson High cells.
Interestingly, the variation in mac-romolecular biosynthesis comes togetherwith an alteration in degradation. Thiswas pointed out in the analysis of geneontology functions, which highlighted“protein catabolic process” as being sig-nificantly altered by mitochondrial con-tent (Supplemental Table S1). Indeed,mitochondria affect RNA andprotein sta-bilities. mRNA stability showed a moder-ate global dependence on mitochondrialcontent (average half-life ∼46 min forHigh versus∼65min for Low conditions)(Supplemental Fig. S7A) and so did pro-tein stability (average half-life ∼15.8 hfor High versus ∼18.4 h for Low condi-tions) (Supplemental Fig. S7B).
Variability in alternative splicingFold changes in total mRNA expressionfrom a given gene may not be indicativeof the impact on the gene function, sincealternatively spliced mRNAs may havedifferent functional implications. Alter-native splicing is a major source of pro-teome diversity (Nilsen and Graveley2010), with important consequences inprocesses such as development (Kalsotraand Cooper 2011) and disease (Cooperet al. 2009). Moreover, the number andrelative abundance of mRNA isoformscan be highly variable, suggesting thatmuch of the alternative splicing (AS)may be a consequence of noise in the
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Figure 6. Effect of mitochondrial content on alternative splicing. (A) Variability in mRNA isoform ex-pression is larger than variability in average gene expression. We show the fraction of isoforms up-regu-lated (FC > 3) or down-regulated (FC < 1/3, red bars) by mitochondrial content compared with thefraction of genes up- or down-regulated (blue bars). (B) Heat map displaying the levels of expressionin High and Low cells (blue to yellow) and the logarithmic FC (red to green). This panel shows a groupof genes in which alternative mRNA types are inverted in High versus Low cells. (C) Scatter plot of log-arithmic FCs for pairs of alternatively spliced transcripts with FC > 10(FC < 1/10). The threshold valueof FC (black squares) defines three domains in which AS is drastically altered by mitochondrial content:(black) both AS forms are overexpressed in mito-high conditions; (blue) both forms are down-regulated;(red) one form is overexpressed and the other is underexpressed. The inset shows the quantification of thefraction of dots in each domain. (D) Schematic representation of the two-stepmodel involving pre-mRNAformation (with transcription rate k) and conversion to alternatively spliced forms with splicing rates α1and α2. These mature mRNA forms can be degraded with rates δ1 and δ2, respectively. (E) Scatter plot oflogarithmic fold changes for pairs of alternatively spliced forms simulated from the two-step model (seeSupplemental Text for details). The threshold in FC and color code is the same as in B. (F) Changes in ATPaffect alternative splicing. Jurkat cells treated for 12 h with deoxyglucose, which affects the splicing ofPTPRC. Under low ATP conditions, the spliced form of PTPRC lacking exons 4, 5, and 6 is overexpressed.For both treatments, each line is a biological replicate.
Guantes et al.
638 Genome Researchwww.genome.org
Cold Spring Harbor Laboratory Press on February 6, 2016 - Published by genome.cshlp.orgDownloaded from
content. Traditionally, mitochondrial mass ismeasured by quanti-fication of the mitochondrial staining with MitoTracker Green(MG) (das Neves et al. 2010). MG faithfully reflects the mitochon-drial mass (as compared bymtDNA quantification) (SupplementalFig. S1A). A problem associated with MG staining is its incom-patibility with the immunostaining of intracellular proteins. Forthat reason, we use instead a probe compatible with the experi-mental procedure (CMXRos). AlthoughCMXRos is a potentiomet-ric probe (and thus a marker of mitochondrial function), in ourconditions it is also a suitable reporter of mitochondrial mass (asshown by its high correlation with MG and several mitochondrialproteins) (Supplemental Fig. S1B–F).
To quantify the influence of mitochondrial content on pro-tein variability, we simultaneously measure mitochondrial andprotein levels using quantitative microscopy in individual HeLacells (Fig. 1B; Iborra and Buckle 2008).
Mitochondrial and protein contents showed an appreciablecorrelation (see Fig. 1C for an example of the protein HK2, all theproteins analyzedgavePearsoncorrelation coefficients in the range0.5–0.8). To calculate howmuchof the total protein variance is dueto covariation with mitochondrial content, we find by linear re-gression the best-fit line (corresponding to the axis of reporter co-variation) (Fig. 1C, black line) and then rotate the data pointsonto the horizontal axis (Fig. 1C, red circles) to obtain the proteindistribution with the correlation removed. We then calculate themitochondrial contribution to variability (MCV) from the spreadof the original and decorrelated distributions as MCV= [1−(IQRrot/IQR)] × 100,where IQR is the interquartile rangeof theorig-inal distributionnormalizedby itsmeanvalue (this gives ameasureof the distribution spread relative to its average value, similar to thecoefficient of variation), and IQRrot is the normalized interquartilerange of the detrended distribution (Fig. 1C, right). This methodprovides an intuitive way to separate the mitochondrial contri-bution from other sources of variability, analogous to the decom-position of total protein variability in intrinsic and extrinsiccontributions (Elowitz et al. 2002). Other statistical measures ofcontribution to variance, such as the fractionof variance explainedby the coefficient of determination, R2, or the F-statistics, yieldvery similar estimates (Supplemental Text; Supplemental Fig. S2).
We chose a set of 24 proteins from housekeeping genes tosample a typical global expression state into normal physiologicalconditions (Fig. 1D,E). To distinguish possible biases due to energymetabolism, we separated the proteins into two groups: proteinsnonrelated to energy metabolism (Fig. 1D) and proteins involvedin energy metabolism (Fig. 1E). In both groups we found that mi-tochondria accounts for ∼50% of protein variability (Fig. 1D,E,thick red dashed lines) with a standard deviation of ∼10%.
Becausemitochondrial content varies along the cell cycle (dasNeves et al. 2010), and so does protein, we asked whether the cor-relation observed betweenmitochondria and protein is a true asso-ciation or, on the other hand, is due to covariation with cell cyclestage. We thus simultaneously stained cells with DAPI (a reporterof cell cycle state), CMXRos, and different protein antibodies.Causal correlation analysis of these experiments showed thatcell cycle, per se, contributes minimally to protein variability(Supplemental Text; Supplemental Fig. S3).
Mitochondrial variability affects different steps of the geneexpression cycleThe data from the previous section show that mitochondrial vari-ability can explain part of the protein noise. To trace back the
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Figure 1. Mitochondrial contribution to protein variability. (A)Differences in themitochondrial content of isogenic cells can act as a glob-al factor generating variability in all steps of gene expression (chromatinremodeling, transcription, and translation) as well as affecting mRNAand protein stabilities. (B) Mitochondrial content (CMXRos) and proteinlevels (the enzyme Hexokinase 2 is shown here) are simultaneously quan-tified in single cells by fluorescence microscopy. (C) Dependence of HK2protein levels as a function of mitochondrial content in a population ofclonal cells (blue dots, r2 = 0.62). CMXRos and protein values are normal-ized by their average levels. We decorrelate protein levels from mitochon-dria by rotating the distribution around the best-fit line (red dots). The boxplots of both distributions are shown on the right. From the ratio of inter-quartile ranges (IQR) of the normal and detrended distributions, we cancalculate the mitochondrial contribution to protein variability in the pop-ulation. (D) Mitochondrial contribution to global variability (MCV) inprotein levels from16 housekeeping genes, none related to energymetab-olism. (E) MCV in protein content from eight genes involved in energyme-tabolism. The thick red dashed line is the average contribution of allproteins. Thin lines are standard deviations. Error bars are standard devia-tions of three independent biological replicates (with 200–400 cells perexperiment).
Guantes et al.
634 Genome Researchwww.genome.org
Cold Spring Harbor Laboratory Press on February 6, 2016 - Published by genome.cshlp.orgDownloaded from
content. Traditionally, mitochondrial mass ismeasured by quanti-fication of the mitochondrial staining with MitoTracker Green(MG) (das Neves et al. 2010). MG faithfully reflects the mitochon-drial mass (as compared bymtDNA quantification) (SupplementalFig. S1A). A problem associated with MG staining is its incom-patibility with the immunostaining of intracellular proteins. Forthat reason, we use instead a probe compatible with the experi-mental procedure (CMXRos). AlthoughCMXRos is a potentiomet-ric probe (and thus a marker of mitochondrial function), in ourconditions it is also a suitable reporter of mitochondrial mass (asshown by its high correlation with MG and several mitochondrialproteins) (Supplemental Fig. S1B–F).
To quantify the influence of mitochondrial content on pro-tein variability, we simultaneously measure mitochondrial andprotein levels using quantitative microscopy in individual HeLacells (Fig. 1B; Iborra and Buckle 2008).
Mitochondrial and protein contents showed an appreciablecorrelation (see Fig. 1C for an example of the protein HK2, all theproteins analyzedgavePearsoncorrelation coefficients in the range0.5–0.8). To calculate howmuchof the total protein variance is dueto covariation with mitochondrial content, we find by linear re-gression the best-fit line (corresponding to the axis of reporter co-variation) (Fig. 1C, black line) and then rotate the data pointsonto the horizontal axis (Fig. 1C, red circles) to obtain the proteindistribution with the correlation removed. We then calculate themitochondrial contribution to variability (MCV) from the spreadof the original and decorrelated distributions as MCV= [1−(IQRrot/IQR)] × 100,where IQR is the interquartile rangeof theorig-inal distributionnormalizedby itsmeanvalue (this gives ameasureof the distribution spread relative to its average value, similar to thecoefficient of variation), and IQRrot is the normalized interquartilerange of the detrended distribution (Fig. 1C, right). This methodprovides an intuitive way to separate the mitochondrial contri-bution from other sources of variability, analogous to the decom-position of total protein variability in intrinsic and extrinsiccontributions (Elowitz et al. 2002). Other statistical measures ofcontribution to variance, such as the fractionof variance explainedby the coefficient of determination, R2, or the F-statistics, yieldvery similar estimates (Supplemental Text; Supplemental Fig. S2).
We chose a set of 24 proteins from housekeeping genes tosample a typical global expression state into normal physiologicalconditions (Fig. 1D,E). To distinguish possible biases due to energymetabolism, we separated the proteins into two groups: proteinsnonrelated to energy metabolism (Fig. 1D) and proteins involvedin energy metabolism (Fig. 1E). In both groups we found that mi-tochondria accounts for ∼50% of protein variability (Fig. 1D,E,thick red dashed lines) with a standard deviation of ∼10%.
Becausemitochondrial content varies along the cell cycle (dasNeves et al. 2010), and so does protein, we asked whether the cor-relation observed betweenmitochondria and protein is a true asso-ciation or, on the other hand, is due to covariation with cell cyclestage. We thus simultaneously stained cells with DAPI (a reporterof cell cycle state), CMXRos, and different protein antibodies.Causal correlation analysis of these experiments showed thatcell cycle, per se, contributes minimally to protein variability(Supplemental Text; Supplemental Fig. S3).
Mitochondrial variability affects different steps of the geneexpression cycleThe data from the previous section show that mitochondrial vari-ability can explain part of the protein noise. To trace back the
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Figure 1. Mitochondrial contribution to protein variability. (A)Differences in themitochondrial content of isogenic cells can act as a glob-al factor generating variability in all steps of gene expression (chromatinremodeling, transcription, and translation) as well as affecting mRNAand protein stabilities. (B) Mitochondrial content (CMXRos) and proteinlevels (the enzyme Hexokinase 2 is shown here) are simultaneously quan-tified in single cells by fluorescence microscopy. (C) Dependence of HK2protein levels as a function of mitochondrial content in a population ofclonal cells (blue dots, r2 = 0.62). CMXRos and protein values are normal-ized by their average levels. We decorrelate protein levels from mitochon-dria by rotating the distribution around the best-fit line (red dots). The boxplots of both distributions are shown on the right. From the ratio of inter-quartile ranges (IQR) of the normal and detrended distributions, we cancalculate the mitochondrial contribution to protein variability in the pop-ulation. (D) Mitochondrial contribution to global variability (MCV) inprotein levels from16 housekeeping genes, none related to energymetab-olism. (E) MCV in protein content from eight genes involved in energyme-tabolism. The thick red dashed line is the average contribution of allproteins. Thin lines are standard deviations. Error bars are standard devia-tions of three independent biological replicates (with 200–400 cells perexperiment).
Guantes et al.
634 Genome Researchwww.genome.org
Cold Spring Harbor Laboratory Press on February 6, 2016 - Published by genome.cshlp.orgDownloaded from
Connecting Variability in Global Transcription Rate toMitochondrial VariabilityRicardo Pires das Neves1,2,3, Nick S. Jones4, Lorena Andreu1, Rajeev Gupta1, Tariq Enver1, Francisco J.
Iborra1,5*
1 Medical Research Council Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, Oxford, United Kingdom, 2 Biocant Center of
Innovation and Biotechnology, Cantanhede, Portugal, 3 Center for Neuroscience and Cell Biology University of Coimbra, Coimbra, Portugal, 4 Department of Physics and
Biochemistry, Oxford Centre for Integrative Systems Biology, CABDyN Complexity Centre, Oxford, United Kingdom, 5 Department of Molecular and Cellular Biology,
Centro Nacional de Biotecnologıa, Consejo Superior de Investigaciones Cientıficas, Madrid, Spain
Abstract
Populations of genetically identical eukaryotic cells show significant cell-to-cell variability in gene expression. However, welack a good understanding of the origins of this variation. We have found marked cell-to-cell variability in average cellularrates of transcription. We also found marked cell-to-cell variability in the amount of cellular mitochondrial mass. Weundertook fusion studies that suggested that variability in transcription rate depends on small diffusible factors. Followingthis, in vitro studies showed that transcription rate has a sensitive dependence on [ATP] but not on the concentration ofother nucleotide triphosphates (NTPs). Further experiments that perturbed populations by changing nutrient levels andavailable [ATP] suggested this connection holds in vivo. We found evidence that cells with higher mitochondrial mass, orhigher total membrane potential, have a faster rate of transcription per unit volume of nuclear material. We also foundevidence that transcription rate variability is substantially modulated by the presence of anti- or prooxidants. Daughterstudies showed that a cause of variability in mitochondrial content is apparently stochastic segregation of mitochondria atdivision. We conclude by noting that daughters that stochastically inherit a lower mitochondrial mass than their sisters haverelatively longer cell cycles. Our findings reveal a link between variability in energy metabolism and variability intranscription rate.
Citation: das Neves RP, Jones NS, Andreu L, Gupta R, Enver T, et al. (2010) Connecting Variability in Global Transcription Rate to Mitochondrial Variability. PLoSBiol 8(12): e1000560. doi:10.1371/journal.pbio.1000560
Academic Editor: Jonathan S. Weissman, University of California San Francisco/Howard Hughes Medical Institute, United States of America
Received June 11, 2010; Accepted October 28, 2010; Published December 14, 2010
Copyright: ! 2010 Pires das Neves et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work has been founded by the Ministerio de Ciencia e Innovacion (Spain) (BFU2009-10792) and the Medical Research Council (UK). The fundershad no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Abbreviations: Br-RNA, RNA containing bromouridine; BrU, bromouridine; BrUTP, bromouridine triphosphate; CV, coefficient of variation; DG, deoxyglucose;DTT, dithiothreitol; FLIP, fluorescence loss in photobleaching; NEM, N-ethylmaleimide; NTP, nucleotide triphosphate; P-S6, phosphorylated ribosomal protein S6;RNA pol II, RNA polymerase II; TMRM, tetramethyl rhodamine methyl ester; YFP, yellow fluorescent protein
* E-mail: [email protected]
Introduction
Genetically identical populations of cells can exhibit cell-to-cellvariations in the amount of individual gene products; this canresult in phenotypic diversity [1,2]. The study of cellular variabilitywas pioneered by Delbruck in the mid-forties, who measureddifferences in the number of phages produced by individualEscherichia coli [3]. Fluctuations in the small numbers of moleculesinvolved in gene expression have been indicated as a source of thisvariation, and current experimental and theoretical approachesseek to anatomize the potential sources of variability, or ‘‘noise’’.Variation between cells could be due to global factors such as cellcycle position or differences in numbers of transcription factors.Such changes can affect all genes and so constitute ‘‘extrinsic’’sources of variability. In contrast, ‘‘intrinsic’’ noise is identified asmolecular variation that occurs at the level of single genes andtheir products [4]. Cell-to-cell variability could be mainly thecombined effect of large amounts of intrinsic variation or might beattributable to more system-wide extrinsic variation. In thefollowing we investigate how global factors can influencetranscription rate across the eukaryotic cell.
Experiments investigating gene expression noise suggest thatgene expression variability has a mix of intrinsic and extrinsicsources [5,6]. Intrinsic noise has been modelled extensively, andwe have a relatively refined idea of its origin in the molecularmachinery of transcription, translation, and degradation [1,2,7,8].The magnitude of extrinsic noise is largest at intermediate levels ofgene expression and dominates when gene expression is high[6,7,9]. However, the sources of extrinsic noise are not as wellcharacterised as those of intrinsic noise [7,10]. Studies carried outin yeast have, for example, suggested cell size, cell shape, cell cyclestage, and fluctuations in an as yet unidentified upstream regulatoras potential sources of extrinsic noise [9,11–13]. While there hasbeen discussion of variability in the process of transcription both inpolymerase binding and in transcription elongation, e.g., [14–16],this is often with the principal aim of understanding intrinsic noise:in the following we will investigate how extrinsic factors mightmodulate transcription rate.
To investigate the origins of global variability in eukaryotic geneexpression we undertook a study of global transcription rate. Wedefine global transcription rate as the average rate of production oftranscripts within the nucleus of a single cell. Our results, obtained
PLoS Biology | www.plosbiology.org 1 December 2010 | Volume 8 | Issue 12 | e1000560
2010,PlosBiology
content. Traditionally, mitochondrial mass ismeasured by quanti-fication of the mitochondrial staining with MitoTracker Green(MG) (das Neves et al. 2010). MG faithfully reflects the mitochon-drial mass (as compared bymtDNA quantification) (SupplementalFig. S1A). A problem associated with MG staining is its incom-patibility with the immunostaining of intracellular proteins. Forthat reason, we use instead a probe compatible with the experi-mental procedure (CMXRos). AlthoughCMXRos is a potentiomet-ric probe (and thus a marker of mitochondrial function), in ourconditions it is also a suitable reporter of mitochondrial mass (asshown by its high correlation with MG and several mitochondrialproteins) (Supplemental Fig. S1B–F).
To quantify the influence of mitochondrial content on pro-tein variability, we simultaneously measure mitochondrial andprotein levels using quantitative microscopy in individual HeLacells (Fig. 1B; Iborra and Buckle 2008).
Mitochondrial and protein contents showed an appreciablecorrelation (see Fig. 1C for an example of the protein HK2, all theproteins analyzedgavePearsoncorrelation coefficients in the range0.5–0.8). To calculate howmuchof the total protein variance is dueto covariation with mitochondrial content, we find by linear re-gression the best-fit line (corresponding to the axis of reporter co-variation) (Fig. 1C, black line) and then rotate the data pointsonto the horizontal axis (Fig. 1C, red circles) to obtain the proteindistribution with the correlation removed. We then calculate themitochondrial contribution to variability (MCV) from the spreadof the original and decorrelated distributions as MCV= [1−(IQRrot/IQR)] × 100,where IQR is the interquartile rangeof theorig-inal distributionnormalizedby itsmeanvalue (this gives ameasureof the distribution spread relative to its average value, similar to thecoefficient of variation), and IQRrot is the normalized interquartilerange of the detrended distribution (Fig. 1C, right). This methodprovides an intuitive way to separate the mitochondrial contri-bution from other sources of variability, analogous to the decom-position of total protein variability in intrinsic and extrinsiccontributions (Elowitz et al. 2002). Other statistical measures ofcontribution to variance, such as the fractionof variance explainedby the coefficient of determination, R2, or the F-statistics, yieldvery similar estimates (Supplemental Text; Supplemental Fig. S2).
We chose a set of 24 proteins from housekeeping genes tosample a typical global expression state into normal physiologicalconditions (Fig. 1D,E). To distinguish possible biases due to energymetabolism, we separated the proteins into two groups: proteinsnonrelated to energy metabolism (Fig. 1D) and proteins involvedin energy metabolism (Fig. 1E). In both groups we found that mi-tochondria accounts for ∼50% of protein variability (Fig. 1D,E,thick red dashed lines) with a standard deviation of ∼10%.
Becausemitochondrial content varies along the cell cycle (dasNeves et al. 2010), and so does protein, we asked whether the cor-relation observed betweenmitochondria and protein is a true asso-ciation or, on the other hand, is due to covariation with cell cyclestage. We thus simultaneously stained cells with DAPI (a reporterof cell cycle state), CMXRos, and different protein antibodies.Causal correlation analysis of these experiments showed thatcell cycle, per se, contributes minimally to protein variability(Supplemental Text; Supplemental Fig. S3).
Mitochondrial variability affects different steps of the geneexpression cycleThe data from the previous section show that mitochondrial vari-ability can explain part of the protein noise. To trace back the
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Figure 1. Mitochondrial contribution to protein variability. (A)Differences in themitochondrial content of isogenic cells can act as a glob-al factor generating variability in all steps of gene expression (chromatinremodeling, transcription, and translation) as well as affecting mRNAand protein stabilities. (B) Mitochondrial content (CMXRos) and proteinlevels (the enzyme Hexokinase 2 is shown here) are simultaneously quan-tified in single cells by fluorescence microscopy. (C) Dependence of HK2protein levels as a function of mitochondrial content in a population ofclonal cells (blue dots, r2 = 0.62). CMXRos and protein values are normal-ized by their average levels. We decorrelate protein levels from mitochon-dria by rotating the distribution around the best-fit line (red dots). The boxplots of both distributions are shown on the right. From the ratio of inter-quartile ranges (IQR) of the normal and detrended distributions, we cancalculate the mitochondrial contribution to protein variability in the pop-ulation. (D) Mitochondrial contribution to global variability (MCV) inprotein levels from16 housekeeping genes, none related to energymetab-olism. (E) MCV in protein content from eight genes involved in energyme-tabolism. The thick red dashed line is the average contribution of allproteins. Thin lines are standard deviations. Error bars are standard devia-tions of three independent biological replicates (with 200–400 cells perexperiment).
Guantes et al.
634 Genome Researchwww.genome.org
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content. Traditionally, mitochondrial mass ismeasured by quanti-fication of the mitochondrial staining with MitoTracker Green(MG) (das Neves et al. 2010). MG faithfully reflects the mitochon-drial mass (as compared bymtDNA quantification) (SupplementalFig. S1A). A problem associated with MG staining is its incom-patibility with the immunostaining of intracellular proteins. Forthat reason, we use instead a probe compatible with the experi-mental procedure (CMXRos). AlthoughCMXRos is a potentiomet-ric probe (and thus a marker of mitochondrial function), in ourconditions it is also a suitable reporter of mitochondrial mass (asshown by its high correlation with MG and several mitochondrialproteins) (Supplemental Fig. S1B–F).
To quantify the influence of mitochondrial content on pro-tein variability, we simultaneously measure mitochondrial andprotein levels using quantitative microscopy in individual HeLacells (Fig. 1B; Iborra and Buckle 2008).
Mitochondrial and protein contents showed an appreciablecorrelation (see Fig. 1C for an example of the protein HK2, all theproteins analyzedgavePearsoncorrelation coefficients in the range0.5–0.8). To calculate howmuchof the total protein variance is dueto covariation with mitochondrial content, we find by linear re-gression the best-fit line (corresponding to the axis of reporter co-variation) (Fig. 1C, black line) and then rotate the data pointsonto the horizontal axis (Fig. 1C, red circles) to obtain the proteindistribution with the correlation removed. We then calculate themitochondrial contribution to variability (MCV) from the spreadof the original and decorrelated distributions as MCV= [1−(IQRrot/IQR)] × 100,where IQR is the interquartile rangeof theorig-inal distributionnormalizedby itsmeanvalue (this gives ameasureof the distribution spread relative to its average value, similar to thecoefficient of variation), and IQRrot is the normalized interquartilerange of the detrended distribution (Fig. 1C, right). This methodprovides an intuitive way to separate the mitochondrial contri-bution from other sources of variability, analogous to the decom-position of total protein variability in intrinsic and extrinsiccontributions (Elowitz et al. 2002). Other statistical measures ofcontribution to variance, such as the fractionof variance explainedby the coefficient of determination, R2, or the F-statistics, yieldvery similar estimates (Supplemental Text; Supplemental Fig. S2).
We chose a set of 24 proteins from housekeeping genes tosample a typical global expression state into normal physiologicalconditions (Fig. 1D,E). To distinguish possible biases due to energymetabolism, we separated the proteins into two groups: proteinsnonrelated to energy metabolism (Fig. 1D) and proteins involvedin energy metabolism (Fig. 1E). In both groups we found that mi-tochondria accounts for ∼50% of protein variability (Fig. 1D,E,thick red dashed lines) with a standard deviation of ∼10%.
Becausemitochondrial content varies along the cell cycle (dasNeves et al. 2010), and so does protein, we asked whether the cor-relation observed betweenmitochondria and protein is a true asso-ciation or, on the other hand, is due to covariation with cell cyclestage. We thus simultaneously stained cells with DAPI (a reporterof cell cycle state), CMXRos, and different protein antibodies.Causal correlation analysis of these experiments showed thatcell cycle, per se, contributes minimally to protein variability(Supplemental Text; Supplemental Fig. S3).
Mitochondrial variability affects different steps of the geneexpression cycleThe data from the previous section show that mitochondrial vari-ability can explain part of the protein noise. To trace back the
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Figure 1. Mitochondrial contribution to protein variability. (A)Differences in themitochondrial content of isogenic cells can act as a glob-al factor generating variability in all steps of gene expression (chromatinremodeling, transcription, and translation) as well as affecting mRNAand protein stabilities. (B) Mitochondrial content (CMXRos) and proteinlevels (the enzyme Hexokinase 2 is shown here) are simultaneously quan-tified in single cells by fluorescence microscopy. (C) Dependence of HK2protein levels as a function of mitochondrial content in a population ofclonal cells (blue dots, r2 = 0.62). CMXRos and protein values are normal-ized by their average levels. We decorrelate protein levels from mitochon-dria by rotating the distribution around the best-fit line (red dots). The boxplots of both distributions are shown on the right. From the ratio of inter-quartile ranges (IQR) of the normal and detrended distributions, we cancalculate the mitochondrial contribution to protein variability in the pop-ulation. (D) Mitochondrial contribution to global variability (MCV) inprotein levels from16 housekeeping genes, none related to energymetab-olism. (E) MCV in protein content from eight genes involved in energyme-tabolism. The thick red dashed line is the average contribution of allproteins. Thin lines are standard deviations. Error bars are standard devia-tions of three independent biological replicates (with 200–400 cells perexperiment).
Guantes et al.
634 Genome Researchwww.genome.org
Cold Spring Harbor Laboratory Press on February 6, 2016 - Published by genome.cshlp.orgDownloaded from
LasenfermedadesrelacionadasamutacionesenelmtDNAseencuentranentrelos
desórdenesgené7cosmasfrecuentes:1:5000
Especiesreac)vasdeoxígeno(ROS)
FOCUS | REVIEW ARTICLENATURE CELL BIOLOGY
In the cytosol, citrate is converted to acetyl CoA by ACLY, which can access several pathways, including conversion to malonyl CoA by the activity of ACC (as described above in ‘Fatty acid oxidation’). Cytosolic citrate is a potent allosteric regulator of ACC, increasing its polymerization and activity83.
Regulation of citrate export may provide a physiological node for the cell to communicate lipid homeostasis to the mitochon-dria. SLC25A1 is sensitive to membrane rigidity, and high levels of cholesterol or acidic phospholipids in the IMM repress mitochon-drial citrate export84. Moreover, fasting causes a 40% reduction in mitochondrial citrate export85. Although these studies indicate that citrate export is affected by lipid abundance, it is unknown if repres-sion of SLC25A1-mediated citrate export affects ACC2 polymeriza-tion and FAS initiation.
Acetyl CoA is required for epigenetic modifications, such as histone acetylation86–88. Thus, fat metabolism may be intimately linked with the epigenetic state, although it is unknown whether the connection is direct. The emerging role of mitochondrial metabolism in epigenetic reprogramming may extend beyond acetyl CoA to include other mitochondrial metabolites such as
succinate, fumarate and ROS, which directly affect the activity of Fe (II)/α -KG-dependent dioxygenases, including hydroxylases, DNA demethylases and histone demethylases89.
Amino acids. The mitochondria are a hub for amino acid synthe-sis, including glutamine, glutamate, alanine, proline and aspartate. Glutamine synthetase (GS) condenses glutamate and ammonia to make glutamine90. GS has been reported to have activity in the cytosol and mitochondria, and its biological role may differ depend-ing on its subcellular localization. GS has a ‘weak’ mitochondrial localization sequence and is imported into the mitochondria in the liver, whereas GS is cytoplasmic in astrocytes91. In glioblastoma, GS generates a source of glutamine for de novo purine synthesis92. However, in breast cancer cells, GS-derived glutamine is not used for de novo nucleotide synthesis93. One possible explanation for this difference is the subcellular localization of GS in these systems.
Glutamate is generated by and utilized as a nitrogen source for numerous reactions94. Glutamate metabolism stratifies in prolifer-ating and quiescent cells; proliferating cells elevate the expression of glutamate-dependent transaminases, whereas quiescent cells
NH3
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Fig. 4 | Mitochondria orchestrate waste management. a, Tumour cells increase nutrient consumption and metabolic fitness relative to healthy tissue, leading to accumulation of waste products in the tumour microenvironment. To manage metabolic waste, cancer cells engage recycling pathways for these metabolic by-products. b, Production and metabolic clearance of ammonia (NH3) in cell metabolism. NH3 is generated by amino acid and nucleotide catabolism. NH3 is assimilated in the mitochondria through GS, GDH, and CPS1. CPS1 initiates the urea cycle for production of the metabolic waste product urea. Urea can be re-catabolized by urease positive bacteria in the microbiome to regenerate NH3. c, Production and metabolic clearance of hydrogen sulfide (H2S) in cell metabolism. H2S is generated by the mammalian enzymes CBS, CSE, 3MST and from the metabolic reactions in the microbiome. H2S is cleared by iterative oxidation catalysed by SQR, TR, and SO. TR utilizes oxidized glutathione (GS–) as a sink for electrons. Oxidations catalysed by SQR and SO are linked to mitochondrial ETC and oxidative phosphorylation. d, Reactions that generate and sequester ROS. ROS are generated in the mitochondria through the ETC and NOX4. SOD2 converts superoxide into the less reactive molecule hydrogen peroxide (H2O2). In the mitochondria, H2O2 is turned over by combined functions of peridoxins (Prx) and thioredoxins (Trx). H2O2 also reacts with Fe+2 (the Fenton reaction) to generated OH in the mitochondria. ROS inflict oxidative damage to proteins in the mitochondria and cytosol, and also function as potent mitogen signalling agents.
FOCUS | REVIEW ARTICLEhttps://doi.org/10.1038/s41556-018-0124-1FOCUS | REVIEW ARTICLENATURE CELL BIOLOGY
© 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
NATURE CELL BIOLOGY | www.nature.com/naturecellbiology
BaseExcisionRepair
Impact of UV-induced mitochondrial DNA damage in cell fate. Manuel Muñoz, PhD
2
Photolyases are white-light-activated enzymes absent in humans but present in many different organisms. The expression of photolyases in the mitochondrial matrix of human keratinocytes will allow us to selectively repair the UV-induced mtDNA lesions by simple exposure to white light, which activates the photolyases. Hence, we will analyse the contribution of UV-induced mtDNA damage in relevant aspects of the cell response to UV exposure like induction of programmed cell death, mitochondrial function, synthesis of key metabolites and modulation of gene expression patterns.
Specific Aims
1. UV-induced mtDNA damage is not repaired in human skin. Using keratinocytes, we will direct the expression of repair enzymes, photolyases from marsupials and plants, to mitochondria so as to control the repair of CPDs and (6-4)PPs, the most conspicuous UV induced DNA lesions. We plan to obtain keratinocytes expressing a single photolyase (CPD or (6-4)PP) as well both photolyases simultaneously.
2. The cell lines generated in the previous point will be use to analyse the impact of UV-induced mtDNA damage in the induction of programmed cell death, mitochondrial activity and nuclear and mitochondrial gene expression patterns.
- Preliminary results
Preliminary results from our group showed that mitochondrial activity, measured as oxygen consumption rates and trans-membrane potential, is reduced in a time dependent manner in human keratinocytes exposed to UV light (Figure 1).
Figure 1. A: O2 consumption levels in human keratinocytes (HaCaT cell line) were measured using an Electrode Oxygraph after UV treatment (UVC 15 J/m2). In all cases O2 consumption were KCN sensitive proving mitochondrial involvement. B: Mitochondrial trans-membrane potential was measured using a Muse cell analyser and a MitoPotential Dye (MCH100110, Millipore). Cells were treated as in A and harvested at different time points after UV irradiation. The 24 hr time point is shown, where membrane depolarization changed from 5.7% in control cells to 57.3% in UV treated cells.
The time dependency suggests that the decrease in mitochondrial function is not a direct consequence of damaging mitochondrial internal membrane or OXPOHS protein factors. Moreover, mRNA steady state level analysis of all genes with functions in respiration or electronic transport (AmiGO, gene ontology database) showed that UV irradiation decreased the expression of mitochondrial genes but not nuclear OXPHOS genes in skin cells (Figure 2).
Mitocondriayestrés
Lab.Muñoz
NER:nucleo)deexcisionrepair
ReparaciónporescisióndenucleóOdos
ManuelJ.MuñozPhD,cáncerdepielymitocondrias
9
LosresultadoshastaaquímostradoscentraronnuestraatenciónenlasconsecuenciasdelUVsobreelgenoma mitocondrial. En este sentido, y como ha sido comentado, hemos corroborado que, adiferencia de lo que ocurre con el ADN nuclear, el daño al ADNmt inducido por luz UV enqueratinocitoshumanosnoesreparado(Figura3).
En consecuencia hipotetizamos que la disminución en la expresión de los factores OXPHOSmitocondriales puede ser una consecuencia del daño al ADNmt inducido por UV, ya que la ARNpolimerasamitocondrial (ARNPmt) no puede sobrepasar las lesiones en el ADN inducidas porUV(Nakanishi et al., 2013). Por el contrario, la ARNPmt puede sobrepasar las lesiones oxidativasinducidasporespeciesreactivasdeloxígeno,poniendoenevidencianuevamentelosefectosdañinosdelaluzUVsobrelafunciónmitocondrial.Estáclaro,porlotanto,queeldañoalADNmtinducidoporUVrepresentaunproblemamayorparalafunciónmitocondrial.PararestaurarlosnivelesdeARNmmitocondrialesquecodificanparasubunidadesdelsistemaOXPHOS,lasmitocondriassevalendesualtonúmerodecopias.HasidoreportadoqueluegodelaexposiciónalaluzUVelnúmerodecopiasdelADNmtaumenta(Gebhardetal.,2014),loquepuedeserinterpretadocomounmecanismoparaasegurar la correcta expresión de ARNs mitocondriales. Concomitantemente, la ADN polimerasamitocondrialintroducemutacionesalreplicarelADNmtdañadoporirradiaciónconUV.Comoyaseha mencionado, defectos en el ADNmt han sido descriptos en varias enfermedades humanas,envejecimientocelularycáncer.Lafunciónmitocondrialafectalaexpresióngénica(patronesdeSA):
HemosdemostradoquelospatronesdeSAsonafectadospor la luz UV (Muñoz et al., 2009 y 2017). Resultarelevante para esta solicitud de subsidio que lairradiacióndequeratinocitosconluzUVafectapatronesde SA en diferentes genes pero, interesantemente, esteefectoesanuladocuandoseagregamediofrescoconunaaltaconcentracióndepiruvatodesodioinmediatamentedespuésdelairradiación(Figura4).Figura 4: Lamodulacióndel SApor luzUV es inhibidaporaltasconcentracionesdepiruvatodesodio,unpromotordelafunciónmitocondrial.Célulasdelapielfueronirradiadascon
La dependencia con el tiempo sugiere que la disminución no es una consecuencia directa del daño a la membrana interna mitocondrial o a los factores proteicos del sistema OXPHOS. Utilizando datos de secuenciación global del transcriptoma obtenidos por nuestro grupo hemos observado que 10 de los 13 factores del sistema OXPHOS codificados en la mitocondria han visto disminuidos sus niveles de ARNm luego de la irradiación con UV. Por el contrario, más del 90% de los factores del sistema OXPHOS codificados en el núcleo no han mostrado cambios en sus niveles deARNm, lo que centra nuestra atención en el genoma mitocondrial. En este sentido, y como ha sido comentado, hemos corroborado que, a diferencia de lo que ocurre con el ADN nuclear, el daño del ADNmt no es reparado (Clayton et al., 1974). En consecuencia hipotetizamos que la disminución en la expresión de los factores OXPHOS mitocondriales puede ser una consecuencia del daño al ADNmt inducido por UV, ya que la ARN polimerasa mitocondrial (ARNPmt) no puede sobrepasar las lesiones en el ADN inducidas por UV (Nakanishi et al., 2013). Por el contrario, la ARNPmt puede sobrepasar las lesiones oxidativas inducidas por especies reactivas del oxígeno, poniendo en evidencia nuevamente los efectos dañinos de la luz UV sobre la función mitocondrial. Está claro, por lo tanto, que el daño al ADNmt inducido por UV representa un problema mayor para la función mitocondrial. Para restaurar los niveles de ARNm mitocondriales que codifican para subunidades del sistema OXPHOS, las mitocondrias se valen de su alto número de copias. Ha sido reportado que luego de la exposición a la luz UV el número de copias del ADNmt aumenta (Gebhard et al., 2014), lo que puede ser interpretado como un mecanismo para asegurar la correcta expresión de ARNs mitocondriales. Concomitantemente, la ADN polimerasa mitocondrial introduce mutaciones al replicar el ADNmt dañado por irradiación con UV. Como ya se ha mencionado, defectos en el ADNmt han sido descriptos en varias enfermedades humanas, envejecimiento celular y cáncer. 2. La función mitocondrial afecta a patrones de SA: Trabajos de nuestro grupo publicados (Muñoz et al., 2009) y no publicados (Muñoz et al., en revisión) demostraron que los patrones de SA son afectados por la luz UV. Resulta relevante para esta solicitud de beca que la irradiación de queratinocitos con luz UV afecta patrones de SA en diferentes genes pero, interesantemente, este efecto es anulado cuando se agrega medio fresco con una alta concentración de piruvato de sodio inmediatamente después de la irradiación (figura 2). ff sFigura 2: La modulación del
a SA por luz UV es inhibida por el piruvato de sodio, un promotor de la función mitocondrial. Células de la piel fueron irradiadas con UV (20 J/m2) e inmediatamente después medio fresco, con distintas concentraciones de piruvato de sodio, fue agregado. Seis horas después el ARN total fue purificado. Se realizó una reacción de retrotranscripción seguida de PCR
utilizado primers flanqueando el evento alternativo. Los productos de amplificación fueron analizados mediante PAGE nativo y cuantificados obteniéndose relaciones de inclusión/exclusión para cada condición. El gráfico muestra la relación inclusión/exclusión en células tratadas con UV dividida la obtenida en células sin irradiar (UV fold effect). A modo de ejemplo se muestran dos eventos alternativos presentes en los genes Gemin 6 y TBX 3.
Nuestra evidencia muestra que la luz UV inhibe la función mitocondrial (figura 1) y que modula los patrones de SA cuando la actividad metabólica mitocondrial no es promovida como, por ejemplo, al utilizar un medio de cultivo sin piruvato (figura 2). Para ahondar en la posibilidad de que la luz UV esté afectando decisiones de SA debido a la alteración de la función mitocondrial, incubamos células de la piel con una baja concentración de oxígeno (3%) de manera tal de disminuir la actividad del sistema OXPHOS. En estas condiciones obervamos que se inducen cambios en el SA (resultados no mostrados) similares a los que producen el tratamiento con luz UV y la posterior incubación en un medio de cultivo sin piruvato. Así,
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Figura 3: ADN nuclear y mitocondrial purificado a partir de células HaCaT inmediatamente (0 hr) o 4 hrs luego de la irradiación con luz UV (20 J/m2) fue utilizado en ensayos de Dot Blot. La reparación de los fotoproductos 6-4 fue revelada utilizando anticuerpos específicos (6-4 PP) mientras que la cantidad total de ADN fue verificada usando anticuerpos anti ADN simple cadena (ssDNA).
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Figura 2: Análisis de expresión de genes involucrados en respiración y transporte electrónico en células HaCaT tratadas con luz UV (20J/m2) o sin tratar (Ctrl) (Box Plot). "Nucleares" incluye el análisis de expresión de casi 100 genes codificados en el genoma nuclear mientras que "Mitocondriales" incluye el análisis de los 13 genes que codifican para factores de la respiración codificados en el genoma mitocondrial. Se muestra la relación UV/Control.
NERausenteenmitocondria
Lab.Muñoz
Ultraviolet image of the Sun Truck Driver
sporadic KSS, PEO and Pearson’s syndrome is extremely high (>80% in affected issues)39. Interestingly, these mutations also accumulate during the lifetime of normal people, albeit at extremely low levels that are detectable only by PCR40 and with different amounts in different tissues41. It appears that these Δ-mtDNAs accumulate during our lifetimes, especially in postmitotic tissues such as the brain, heart and muscle42–44. Although they arise spontaneously as the result of somatic muta-tion, each individual species of Δ-mtDNA has the capacity to expand clonally45; this expansion is particu-larly noticeable in muscle from aged normal individuals46 (and in patients with Mendelian PEO)47. Although each individual species of Δ-mtDNA may represent less than 0.1% of total mtDNA43,44, sufficient numbers of different Δ-mtDNA species accumulate so that, depending on their tissue distribution and concentration48, they can have deleterious consequences49.
Although Δ-mtDNAs clearly accumulate in ageing, the evidence that the same holds for point mutations is weaker43. There are many ongoing studies aimed at detecting somatic mtDNA mutations, but it still remains uncertain whether these mutations cause overt disease or whether they have a major role in normal ageing. Perhaps studies of experimental models that age more rapidly than humans (for example, flies, worms and even yeast) will shed light on this question.
mtDNA mutations in late-onset neurodegenerative dis-ease. There is a growing interest in the potential role of mitochondrial dysfunction in general, and of mtDNA mutations in particular, in late-onset neurodegen-erative diseases, such as Alzheimer’s, Parkinson’s and Huntington’s diseases, amyotrophic lateral sclerosis and hereditary spastic paraplegias50. Although there seems to be little doubt of a mitochondrial connection, it seems to relate more to problems of mitochondrial dynamics (for example, fission, fusion, distribution and anchorage) or quality control (for example, autophagy) than to mutations in mtDNA50. However, there is one neurodegenerative disorder in which mtDNA mutations may have a role (albeit probably a secondary one), and that is Parkinson’s disease. Studies of proteins mutated in familial Parkinson’s disease — for example, PINK1, a mitochondrially targeted kinase51, and parkin, a cyto-solic ubiquitin ligase that translocates to low-Δψm dys-functional mitochondria52 — have implicated altered mitochondrial quality control as an underlying element in the pathogenesis of both the familial and sporadic forms of the disease. However, it has also been found that the substantia nigra — the brain region that is most affected in Parkinson’s disease (and the main site of dopaminergic neuron degeneration in patients with Parkinson’s disease) — harbours substantial levels of the types of Δ-mtDNAs found in KSS and in normal age-ing53,54. This finding has led to the hypothesis that these somatically produced Δ-mtDNAs may eventually cause bioenergetic deficit, leading to the disease. This hypoth-esis has received indirect support from studies of mice in which mtDNA deficiency was induced specifically in the substantia nigra and that displayed many of the
Table 1 | mtDNA mutations in human primary respiratory chain disorders
Gene product Number of mutations Main disorder
rRNAs
12S rRNA 5 Deafness
16S rRNA 1 Atypical MELAS
tRNAs
tRNAAla 3 Myopathy
tRNAArg 2 Various
tRNAAsn 5 Myopathy
tRNAAsp 2 Various
tRNACys 3 Various
tRNAGln 3 Various
tRNAGlu 7 Reversible respiratory chain deficiency
tRNAGly 3 Various
tRNAHis 4 Various
tRNAIle 14 PEO
tRNALeu(CUN) 8 Myopathy
tRNALeu(UUR) 23 MELAS
tRNALys 14 MERRF
tRNAMet 2 Various
tRNAPhe 14 Myopathy
tRNAPro 5 Multisystem
tRNASer(AGY) 4 Myopathy
tRNASer(UCN) 12 Myopathy; deafness
tRNAThr 2 Various
tRNATrp 12 Encephalomyopathy
tRNATyr 4 Myopathy
tRNAVal 6 Multisystem
Polypeptides
ATP synthase 6 13 NARP or MILS
ATP synthase 8 2 Various
COX I 10 Various
COX II 8 Various
COX III 6 Myopathy
Cytochrome b 21 Sporadic myopathy
ND1 16 MELAS; LHON
ND2 3 Various
ND3 5 Leigh’s syndrome
ND4 5 LHON
ND4L 1 LHON
ND5 12 MELAS
ND6 11 LHON
This is a ‘minimal’ list that was compiled by the authors. A more comprehensive list (>300 mutations) is available at MITOMAP, which contains confirmed pathogenic mutations (including those selected for this table), those that are possibly associated with pathology and those that appear to confer a higher risk of developing disease or are modifiers of disease presentation. COX, cytochrome c oxidase; LHON, Leber’s hereditary optic neuropathy; MELAS, mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes; MERRF, myoclonus epilepsy and ragged red fibres; MILS, maternally inherited Leigh’s syndrome; mtDNA, mitochondrial DNA; NARP, neuropathy, ataxia and retinitis pigmentosa; ND, NADH-ubiquinone oxidoreductase; PEO, progressive external ophthalmoplegia; rRNA, ribosomal RNA.
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© 2012 Macmillan Publishers Limited. All rights reserved
sporadic KSS, PEO and Pearson’s syndrome is extremely high (>80% in affected issues)39. Interestingly, these mutations also accumulate during the lifetime of normal people, albeit at extremely low levels that are detectable only by PCR40 and with different amounts in different tissues41. It appears that these Δ-mtDNAs accumulate during our lifetimes, especially in postmitotic tissues such as the brain, heart and muscle42–44. Although they arise spontaneously as the result of somatic muta-tion, each individual species of Δ-mtDNA has the capacity to expand clonally45; this expansion is particu-larly noticeable in muscle from aged normal individuals46 (and in patients with Mendelian PEO)47. Although each individual species of Δ-mtDNA may represent less than 0.1% of total mtDNA43,44, sufficient numbers of different Δ-mtDNA species accumulate so that, depending on their tissue distribution and concentration48, they can have deleterious consequences49.
Although Δ-mtDNAs clearly accumulate in ageing, the evidence that the same holds for point mutations is weaker43. There are many ongoing studies aimed at detecting somatic mtDNA mutations, but it still remains uncertain whether these mutations cause overt disease or whether they have a major role in normal ageing. Perhaps studies of experimental models that age more rapidly than humans (for example, flies, worms and even yeast) will shed light on this question.
mtDNA mutations in late-onset neurodegenerative dis-ease. There is a growing interest in the potential role of mitochondrial dysfunction in general, and of mtDNA mutations in particular, in late-onset neurodegen-erative diseases, such as Alzheimer’s, Parkinson’s and Huntington’s diseases, amyotrophic lateral sclerosis and hereditary spastic paraplegias50. Although there seems to be little doubt of a mitochondrial connection, it seems to relate more to problems of mitochondrial dynamics (for example, fission, fusion, distribution and anchorage) or quality control (for example, autophagy) than to mutations in mtDNA50. However, there is one neurodegenerative disorder in which mtDNA mutations may have a role (albeit probably a secondary one), and that is Parkinson’s disease. Studies of proteins mutated in familial Parkinson’s disease — for example, PINK1, a mitochondrially targeted kinase51, and parkin, a cyto-solic ubiquitin ligase that translocates to low-Δψm dys-functional mitochondria52 — have implicated altered mitochondrial quality control as an underlying element in the pathogenesis of both the familial and sporadic forms of the disease. However, it has also been found that the substantia nigra — the brain region that is most affected in Parkinson’s disease (and the main site of dopaminergic neuron degeneration in patients with Parkinson’s disease) — harbours substantial levels of the types of Δ-mtDNAs found in KSS and in normal age-ing53,54. This finding has led to the hypothesis that these somatically produced Δ-mtDNAs may eventually cause bioenergetic deficit, leading to the disease. This hypoth-esis has received indirect support from studies of mice in which mtDNA deficiency was induced specifically in the substantia nigra and that displayed many of the
Table 1 | mtDNA mutations in human primary respiratory chain disorders
Gene product Number of mutations Main disorder
rRNAs
12S rRNA 5 Deafness
16S rRNA 1 Atypical MELAS
tRNAs
tRNAAla 3 Myopathy
tRNAArg 2 Various
tRNAAsn 5 Myopathy
tRNAAsp 2 Various
tRNACys 3 Various
tRNAGln 3 Various
tRNAGlu 7 Reversible respiratory chain deficiency
tRNAGly 3 Various
tRNAHis 4 Various
tRNAIle 14 PEO
tRNALeu(CUN) 8 Myopathy
tRNALeu(UUR) 23 MELAS
tRNALys 14 MERRF
tRNAMet 2 Various
tRNAPhe 14 Myopathy
tRNAPro 5 Multisystem
tRNASer(AGY) 4 Myopathy
tRNASer(UCN) 12 Myopathy; deafness
tRNAThr 2 Various
tRNATrp 12 Encephalomyopathy
tRNATyr 4 Myopathy
tRNAVal 6 Multisystem
Polypeptides
ATP synthase 6 13 NARP or MILS
ATP synthase 8 2 Various
COX I 10 Various
COX II 8 Various
COX III 6 Myopathy
Cytochrome b 21 Sporadic myopathy
ND1 16 MELAS; LHON
ND2 3 Various
ND3 5 Leigh’s syndrome
ND4 5 LHON
ND4L 1 LHON
ND5 12 MELAS
ND6 11 LHON
This is a ‘minimal’ list that was compiled by the authors. A more comprehensive list (>300 mutations) is available at MITOMAP, which contains confirmed pathogenic mutations (including those selected for this table), those that are possibly associated with pathology and those that appear to confer a higher risk of developing disease or are modifiers of disease presentation. COX, cytochrome c oxidase; LHON, Leber’s hereditary optic neuropathy; MELAS, mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes; MERRF, myoclonus epilepsy and ragged red fibres; MILS, maternally inherited Leigh’s syndrome; mtDNA, mitochondrial DNA; NARP, neuropathy, ataxia and retinitis pigmentosa; ND, NADH-ubiquinone oxidoreductase; PEO, progressive external ophthalmoplegia; rRNA, ribosomal RNA.
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884 | DECEMBER 2012 | VOLUME 13 www.nature.com/reviews/genetics
© 2012 Macmillan Publishers Limited. All rights reserved
sporadic KSS, PEO and Pearson’s syndrome is extremely high (>80% in affected issues)39. Interestingly, these mutations also accumulate during the lifetime of normal people, albeit at extremely low levels that are detectable only by PCR40 and with different amounts in different tissues41. It appears that these Δ-mtDNAs accumulate during our lifetimes, especially in postmitotic tissues such as the brain, heart and muscle42–44. Although they arise spontaneously as the result of somatic muta-tion, each individual species of Δ-mtDNA has the capacity to expand clonally45; this expansion is particu-larly noticeable in muscle from aged normal individuals46 (and in patients with Mendelian PEO)47. Although each individual species of Δ-mtDNA may represent less than 0.1% of total mtDNA43,44, sufficient numbers of different Δ-mtDNA species accumulate so that, depending on their tissue distribution and concentration48, they can have deleterious consequences49.
Although Δ-mtDNAs clearly accumulate in ageing, the evidence that the same holds for point mutations is weaker43. There are many ongoing studies aimed at detecting somatic mtDNA mutations, but it still remains uncertain whether these mutations cause overt disease or whether they have a major role in normal ageing. Perhaps studies of experimental models that age more rapidly than humans (for example, flies, worms and even yeast) will shed light on this question.
mtDNA mutations in late-onset neurodegenerative dis-ease. There is a growing interest in the potential role of mitochondrial dysfunction in general, and of mtDNA mutations in particular, in late-onset neurodegen-erative diseases, such as Alzheimer’s, Parkinson’s and Huntington’s diseases, amyotrophic lateral sclerosis and hereditary spastic paraplegias50. Although there seems to be little doubt of a mitochondrial connection, it seems to relate more to problems of mitochondrial dynamics (for example, fission, fusion, distribution and anchorage) or quality control (for example, autophagy) than to mutations in mtDNA50. However, there is one neurodegenerative disorder in which mtDNA mutations may have a role (albeit probably a secondary one), and that is Parkinson’s disease. Studies of proteins mutated in familial Parkinson’s disease — for example, PINK1, a mitochondrially targeted kinase51, and parkin, a cyto-solic ubiquitin ligase that translocates to low-Δψm dys-functional mitochondria52 — have implicated altered mitochondrial quality control as an underlying element in the pathogenesis of both the familial and sporadic forms of the disease. However, it has also been found that the substantia nigra — the brain region that is most affected in Parkinson’s disease (and the main site of dopaminergic neuron degeneration in patients with Parkinson’s disease) — harbours substantial levels of the types of Δ-mtDNAs found in KSS and in normal age-ing53,54. This finding has led to the hypothesis that these somatically produced Δ-mtDNAs may eventually cause bioenergetic deficit, leading to the disease. This hypoth-esis has received indirect support from studies of mice in which mtDNA deficiency was induced specifically in the substantia nigra and that displayed many of the
Table 1 | mtDNA mutations in human primary respiratory chain disorders
Gene product Number of mutations Main disorder
rRNAs
12S rRNA 5 Deafness
16S rRNA 1 Atypical MELAS
tRNAs
tRNAAla 3 Myopathy
tRNAArg 2 Various
tRNAAsn 5 Myopathy
tRNAAsp 2 Various
tRNACys 3 Various
tRNAGln 3 Various
tRNAGlu 7 Reversible respiratory chain deficiency
tRNAGly 3 Various
tRNAHis 4 Various
tRNAIle 14 PEO
tRNALeu(CUN) 8 Myopathy
tRNALeu(UUR) 23 MELAS
tRNALys 14 MERRF
tRNAMet 2 Various
tRNAPhe 14 Myopathy
tRNAPro 5 Multisystem
tRNASer(AGY) 4 Myopathy
tRNASer(UCN) 12 Myopathy; deafness
tRNAThr 2 Various
tRNATrp 12 Encephalomyopathy
tRNATyr 4 Myopathy
tRNAVal 6 Multisystem
Polypeptides
ATP synthase 6 13 NARP or MILS
ATP synthase 8 2 Various
COX I 10 Various
COX II 8 Various
COX III 6 Myopathy
Cytochrome b 21 Sporadic myopathy
ND1 16 MELAS; LHON
ND2 3 Various
ND3 5 Leigh’s syndrome
ND4 5 LHON
ND4L 1 LHON
ND5 12 MELAS
ND6 11 LHON
This is a ‘minimal’ list that was compiled by the authors. A more comprehensive list (>300 mutations) is available at MITOMAP, which contains confirmed pathogenic mutations (including those selected for this table), those that are possibly associated with pathology and those that appear to confer a higher risk of developing disease or are modifiers of disease presentation. COX, cytochrome c oxidase; LHON, Leber’s hereditary optic neuropathy; MELAS, mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes; MERRF, myoclonus epilepsy and ragged red fibres; MILS, maternally inherited Leigh’s syndrome; mtDNA, mitochondrial DNA; NARP, neuropathy, ataxia and retinitis pigmentosa; ND, NADH-ubiquinone oxidoreductase; PEO, progressive external ophthalmoplegia; rRNA, ribosomal RNA.
REVIEWS
884 | DECEMBER 2012 | VOLUME 13 www.nature.com/reviews/genetics
© 2012 Macmillan Publishers Limited. All rights reserved
EnfermedadesasociadasamutacionesenelmtDNA