Ph.D Martin A Cohn
email: martin.cohn@bioch.ox.ac.uk

Keywords: [Cryo electron microscopy, Structural biology, Single particle image analysis, Fanconi, FANCD2/FANCI, Phosphorylase kinase]

Chapter I Introduction

During my thesis work, I have investigated the structure of two protein complexes, the FANCD2/FANCI complex and the Phosphorylase Kinase (PhK). Both complexes were studied using cryo electron microscopy combined with image analysis. The former project (FANCD2/FANCI complex) was performed using conventional electron microscope and the images were taken using CCD camera. The PhK project was performed using state of the art electron microscope and direct electron detection camera. Our collaborator for FANCD2/FANCI complex is Dr Martin Cohn and its team at the University of Oxford (Department of Biochemistry). Concerning the Phosphorylase Kinase, Prof. Gerald Carlson and Dr Owen Nadeau purified the complex and the images were taken in the Laboratory of De Thomas Walz at the Harvard Medical School, Boston.

1.1 FANCD2/FANCI complex 1.1.1 The Maintenance of the genome and the DNA damage response

One of the most critical biological processes is protection and maintenance of the genome. Human chromosomes are constantly exposed to a number of insults and challenges, originating from both intra-and extra-cellular sources. Rapid and effective cellular responses to these diverse genotoxic stresses are essential to ensure genomic stability.

When confronted with DNA damage, the eukaryotic cell has several options.

i) It can arrest its own DNA synthesis, thus allowing DNA repair to occur.

ii) It can continue to replicate its DNA and bypass the damage.

iii) Or, it can surrender to the damage and activate its own demise.

The cellular decision-making process, known as the DNA Damage Response (DDR), is a complex network of signal transduction pathways, involving a vast array of sensor, transducer, and effector proteins [START_REF] Harper | The DNA Damage Response: Ten Years after[END_REF] There are several DNA repair pathways in human cells, including [START_REF] Kennedy | DNA Repair Pathways in Clinical Practice: Lessons from Pediatric Cancer Susceptibility Syndromes[END_REF] -base excision repair (BER),

-mismatch repair (MMR),

-nucleotide excision repair (NER)

-homologous recombination (HR)

-nonhomologous end-joining (NHEJ)

-translesion synthesis (TLS)

These pathways deal with different types of DNA damage. For instance, the HR and NHEJ pathways repair double-strand breaks, whereas the BER pathway primarily repairs damaged single base residues. The repair pathways also have differential importance, depending on the cell type or the cell-cycle phase. For instance, rapidly proliferating cells with a high S phase fraction are more likely to employ HR repair, whereas cells in the G1 phase repair double-strand breaks mainly by NHEJ. A combination of NER, HR, and TLS pathways is required to repair complex DNA lesions, such as DNA crosslinks. The Fanconi Anemia pathway is believed to coordinate these three repair pathways [START_REF] Mirchandani | The Fanconi anemia/BRCA Pathway: A Coordinator of Cross-Link Repair[END_REF].

The specific, sequential, and rapid recruitment of these DNA repair proteins is essential for a successful response to the particular DNA damage. Once the relevant proteins are assembled onto chromatin, these complexes provide the checkpoint activity for halting DNA replication or the DNA repair activity for repairing the damaged bases. Accordingly, the eukaryotic cell has established elaborate mechanisms for the regulated loading of proteins onto damaged chromatin; their disruption results in genomic instability and, in many cases, predisposition of the cell to malignant transformation. Of note, many of these mechanisms require protein ubiquitination and deubiquitination. Failure in these DNA repair pathways is detrimental and often underlies severe genetic diseases and syndromes in human. For example, an interference with a DNA double strand break repair pathway is associated with Nijmegen breakage syndrome, Werner's syndrome and familial breast cancer susceptibility, whereas abrogation of DNA Trans Lesion Synthesis by DNA polymerases is directly linked to the skin cancer syndrome Xeroderma Pigmentosum variant.

Interestingly, the importance of ubiquitin-mediated protein loading onto damaged chromatin in the DNA damage response was elucidated largely through the systematic study of human genetic diseases. The major human hereditary breast cancer susceptibility gene product, BRCA1, for example, is critical for the cellular response to ionizing radiation.

Also, the genetic disease, Fanconi Anemia (FA), is a chromosome instability syndrome involving defective chromatin loading of DNA repair proteins. Like other DNA damage response pathways, regulated ubiquitination and deubiquitination is critical for an intact FA pathway [START_REF] Cohn | Chromatin Recruitment of DNA Repair Proteins: Lessons from the Fanconi Anemia and Double-Strand Break Repair Pathways[END_REF]. In this work we focused on the Fanconi Anemia pathway, which is crucial for the interstrand crosslinks (ICL) repair.

The Fanconi Anemia Pathway Regulates the Loading of DNA Repair

Complexes

The Fanconi anemia (FA) pathway is critical for the cellular response to toxic DNA interstrand crosslinks (ICLs). When this FA pathway is deregulated it causes the FA disease.

FA is a recessive cancer predisposition and developmental syndrome that is characterized by hypersensitivity to DNA interstrand crosslinking agents [START_REF] Cohn | Chromatin Recruitment of DNA Repair Proteins: Lessons from the Fanconi Anemia and Double-Strand Break Repair Pathways[END_REF].

In Fanconi Anemia Pathway, a complex machinary is involved and protein complexes work together to ensure the repair of interstrand crosslinks, a process that likely involves nucleotide excision repair, translesion synthesis, and homologous recombination. Central to the pathway are the FANCD2 and FANCI proteins. The various steps in this pathway process are beginning to emerge although the molecular mechanism of how the two proteins coordinate the DNA repair responses remains elusive. Basically the various steps are described below:

-The FANCD2 and FANCI proteins are phosphorylated on multiple amino acid residues.

-The phosphorylated FANCD2 and FANCI proteins are monoubiquitinated by the FA core complex, containing eight proteins (FANCA, FANCB, FANCC, FANCE, FANCF, FANCG, FANCL, and FANCM protein subunits. FANCL is the E3 ligase subunit).

-In the absence of genotoxic stress, FANCD2 and FANCI are deubiquitinated by the USP1/UAF1 complex to prevent the accumulation of monoubiquitinated FANCD2 and FANCI.

-In response to DNA damage, the level of USP1/UAF1 complex decreases, resulting in the cellular increase in ubiquitinated FANCD2 and FANCI.

-After ubiquitination, FANCD2, FANCI and the remaining eight FA proteins are recruited to the ICLs (Ciccia et al. 2007).

Recently the team of Martin Cohn and co-authors with whom we are collaborating in this project have identified and shown that the protein UHRF1 is recruited very quickly to DNA interstrand crosslinks in vivo, and that the recruitment both precedes and is required for the recruitment of FANCD2 and FANCI to the ICL (Liang et al. 2015), figure 1.1.

The goal of my thesis work is to identify the structural determinants on the FANCD2/FANCI complex and its recruitment to the DNA interstrand crosslinks. (Liang et al. 2015) .

Phosphorylase Kinase 1.2.1 Kinases and kinome

The protein kinases comprise a huge family of regulatory enzymes, they are involved in regulating virtually all aspects of cell physiology, from progression through the cell cycle to programmed cell death and most cellular events in between including mediation of the majority of signal transduction pathways in eukaryotic cells, transcription, metabolism, cellcell regulation, cellular shape, mobility of the cell, differentiation and apoptosis.

In 2012, Gerard Manning and colleagues [START_REF] Manning | The Protein Kinase Complement of the Human Genome[END_REF]) identified 518 putative protein kinases also known as the human kinome from the human genome. Human kinome (Figure 1.2) represents a little less than 2% of all human genes. Since, other kinomes have been determined for rice [START_REF] Dardick | The Rice Kinase Database. A Phylogenomic Database for the Rice Kinome[END_REF]), several fungi [START_REF] King | The Genome of the Choanoflagellate Monosiga Brevicollis and the Origin of Metazoans[END_REF][START_REF] Manning | The Protist, Monosiga Brevicollis, Has a Tyrosine Kinase Signaling Network More Elaborate and Diverse than Found in Any Known Metazoan[END_REF], nematodes, insects, sea urchins, Dictyostelium discoideum, zebra fish, malaria [START_REF] Talevich | An Evolutionary Perspective on the Kinome of Malaria Parasites[END_REF]) and the process of infection by Mycobacterium tuberculosis.

Reversible phosphorylation of proteins is an important regulatory mechanism that occurs in both prokaryotic and eukaryotic organisms. Kinases phosphorylate proteins and phosphatases dephosphorylate proteins. Many enzymes and receptors are switched "on" or "off" by phosphorylation and dephosphorylation. Reversible phosphorylation results in a conformational change in the structure in many enzymes and receptors, causing them to become activated or deactivated. Kinases are enzymes that catalyze phosphorylation reactions on certain amino acids such as serine, threonine and tyrosine. The proliferation of cancer cells often involves alterations in kinase activity in signaling pathways. As such, human protein kinases represent important targets in drug design [START_REF] Metz | Navigating the Kinome[END_REF]Johnson 2009a).

The catalytic domain of the kinases is common among all the kinases. The first 3D structure of a kinase catalytic domain elucidated is that of CAPK (CAMP-Dependent Protein Kinase AGC group code PDB: 2CPK), using X-ray crystallography in 1991 [START_REF] Knighton | Crystal-Structure of the Catalytic Subunit of Cyclic Adenosine-Monophosphate Dependent Protein-Kinase[END_REF]. In 1993, a second atomic structure is obtained where the catalytic domain is cocrystallized with a molecule of ATP and a substrate peptide [START_REF] Zheng | Crystal-Structure of the Catalytic Subunit of Camp-Dependent Protein-Kinase Complexed with Mgatp and Peptide Inhibitor[END_REF]. The kinase catalytic domain has a characteristic bilobal architecture (figure 1. 3). A smaller N-terminal lobe, formed principally from  sheets, is connected to a larger C-terminal lobe formed mainly from  helices. The two lobes are joined by a hinge region. The catalytic site is juxtaposed between small N-terminal and large C-terminal globular domains. Despite the highly conserved structure of the catalytic domain, different protein kinases have unique and diverse physiological functions. Some are quite promiscuous (the multisubstrate protein kinases) and it is not yet known what constitutes a "good" physiological substrate for many of them. Kinases catalyses the transfer of the gamma-phosphate from a nucleoside triphosphate donor to a substrate. The exact mechanism remains to be established. The protein kinases transfer phosphate specifically to the side-chain hydroxyl of a serine, threonine, tyrosine residue in substrate proteins. Though this reaction is relatively simple, considerable complexity is evident in the regulation of kinase activity. The activity of the protein kinases is generally subject to strict regulatory control (i.e., unregulated kinases are usually detrimental and are the underlying cause of some serious human diseases, such as cancer).

The in vivo activity of a kinase is determined by various signals that impinge on its regulatory domain. Some kinases have more than one type of regulatory subunit or domain and their phosphotransfer activity can be affected by more than one type of signal. Conversely, some signals can activate numerous kinases. Most enzymes catalyse a single reaction involving well-defined substrates. Protein kinases are somewhat unique in that some have multiple substrates. These properties make the task of identifying the physiological functions of a given protein kinase incredibly difficult. Some kinases have very simple structures and modes of regulation, whereas others are exceedingly complex. The protein kinases have evolved into such a large and functionally diverse enzyme family because they perform so exquisitely their respective regulatory functions.

Of all the protein kinases characterized to date, PhK (1.3MDalton) is the most complex in structure. Coupled to its complexity in structure is its complexity of regulation.

Yet its complexity seems to belie its function since the only characterized substrate to date in vivo is glycogen phosphorylase (GP) and glycogen synthase (GS), and even though it has multiple mechanisms of control it is not apparent why the protein should be so large and have a structure so complex [START_REF] Brushia | Phosphorylase Kinase: The Complexity of Its Regulation Is Reflected in the Complexity of Its Structure[END_REF]. However, various proteins have been reported that are phosphorylated by PhK in vitro including troponin I, troponin T, casein, histoneH1, neurogranin, neuromodulin, its own  subunit and the Tau protein associated with Alzheimer disease [START_REF] Akatsuka | Phosphorylation of Rat-Liver Glycogen-Synthase by Phosphorylase-Kinase[END_REF][START_REF] Kilimann | Multiple Activities On Phosphorylase-Kinase .2. Different Specificities Toward The Protein Substrates Phosphorylase-B, Troponin, And Phosphorylase-Kinase[END_REF][START_REF] Depaoliroach | Phosphorylase-Kinase from Rabbit Skeletal-Muscle -Phosphorylation of Kappa-Casein[END_REF][START_REF] Tabuchi | Phosphorylation of Calf Thymus H-1 Histone by Muscle Glycogen-Phosphorylase Kinase[END_REF][START_REF] Pearson | Protein-Kinase Phosphorylation Site Sequences and Consensus Specificity Motifs -Tabulations[END_REF][START_REF] Paudel | The Regulatory Ser(262) of Microtubule-Associated Protein Tau Is Phosphorylated by Phosphorylase Kinase[END_REF][START_REF] Paudel | Phosphorylase-Kinase Phosphorylates the Calmodulin-Binding Regulatory Regions of Neuronal Tissue-Specific Proteins B-50 (gap-43) and Neurogranin[END_REF].

Glycogen metabolism

Although not the first protein kinase to be observed, phosphorylase b kinase (now PhK EC 2.7.11.19 but initially PhK EC 2.7.1.38 [created in 1961deleted in 2005]) was the first to be purified, have its specific function determined, and have its physicochemical properties characterized (Pickett-Gies and Walsh 1985; [START_REF] Kennedy | The Enzymatic Phosphorylation of Proteins[END_REF]. PhK catalyzes the phosphorylation and activation of glycogen phosphorylase. Which consequentially promotes glycogenolysis. Glycogen phosphorylase b (dephospho form) is converted to glycogen phosphorylase a in the reaction catalyzed by phosphorylase kinase. The regulation of glycogenolysis (glycogen metabolism) is the only physiological function that has so far been established for PhK, albeit other key roles for this enzyme may well emerge.

Glycogen metabolism is a relatively simple biological process within the cells for the production of an oxidable energy source, glucose (Figure 1.4). In organisms, glycogen metabolism is the main mechanism for glucose homeostasis. However, the regulation of glycogenolysis and synthesis of glycogen is a rather complex mechanism which involves the interplay of several enzymes (Figure 1.5 [START_REF] King | Glycogen, Starch and Sucrose Synthesis[END_REF]) and a phosphorylation/dephosphorylation cascade.

Glycogen synthesis and degradation is determined by glycogen synthase (GS) and glycogen phosphorylase (GP), respectively. The activity of GS is inhibited by the presence of GPa (a phosphorylated form of GP which is fully active) since GPa is a potent inhibitor of glycogen synthase phosphatase which catalyses the dephosphorylarion and activation of GS. This way, the phosphorylation state of GP determines not only the rate of glycogenolysis but also the rate of glycogen synthesis. Thus, phosphorylase kinase (PhK) the enzyme that catalyses the phosphorylation of GP has a pivotal role in the mobilization of glycoge storage and glucose homeostasis. GP catalyses the phospholytic breakdown of the -1,4 glycoside bonds in glycogen to produce glucose-1phosphate (Glc-1-P) Figure 1.4. It starts from the non-reducing end of glycogen and continues until it reaches four glucose residues away from a-1,6 branch. GP has a cofactor, pirydoxal 5phosphate (PLP, derived from vitamin B6). In response to adrenaline or glucagon, PhK catalyses the Ca 2+ dependent phosphorylation of a serine residue (Ser14) in GP and thus transforming GPb (low activity, low substrate affinity) to the GPa (high activity, high substrate affinity) [START_REF] Oikonomakos | Glycogen Phosphorylase as a Molecular Target for Type 2 Diabetes Therapy[END_REF]. PhK is activated also by phosphorylation from protein kinase A, which is activated by cyclic AMP. The reverse reaction of Schematic two-dimensional cross-sectional view of glycogen: A core protein of glycogenin is surrounded by branches of glucose units. The entire globular granule may contain around 30,000 glucose units.

C:

1,4-α-glycosidic and 1,6-glycosidic linkages in the glycogen oligomer D:

The phosphorylation of glycogen phosphorylase (Gpb) on a serine residue by phosphorylase kinase (PHK) is essential for the enzyme to be active. Glycogen phosphorylase activated catalyzes the phosphorolysis of the α-1,4 glycosidic bond that unites the last glucose of a branch to the rest of the glycogen molecule. The detached glucose receives the phosphoryl group on its carbon 1 (glucose 1-phosphate), while the following glucose on the branch retrieves the secondary alcohol function on his Carbone 4. In muscle, glucose 1-phosphate is then converted into glucose 6-phosphate by the action of phosphoglucomutase. the glucose 6-phosphate is the substrate for glycolysis. In liver, glucose 6-phosphate is catalysed into glucose by glucose 6-phosphatase and the glucose is released in blood circulation to maintain blood glucose at correct level. Illustration form https://en.wikipedia.org/wiki/Glycogen dephosphorylation that inactivates GP is catalysed by protein phosphatase 1 (PP1), an enzyme that is regulated in response to insulin. The conversion of GPa to GPb relieves the allosteric inhibition that GPa exerts on the glycogen-associated PP1, which convert glycogen synthase D to the I form, thus allowing the phosphatase to stimulate the synthesis of glycogen. The PHK-a will in turn catalyze the activation of its substrate, the glycogen phosphorylaseb (denoted here phosphorylase-b) into the active form, phosphorylase-a. This reaction is energy-costing: one molecule of ATP is hydrolyzed and results in the release of ADP. The PhK-a and GPa can return to their inactive state by the hydrolysis of the phosphate group by phosphoprotein phosphatase, a group phosphate mineral is then released. The action of the phosphatase is inhibited by the PPI-1, the "Phosphoprotein phosphatase inhibitor-1 "in English. The positive or negative sign indicates the nature of the action of different effector illustrated above.

Illustriation from "Encyclopedia of Life Sciences", version 2002. 'Glycogen, Starch and Sucrose Synthesis' Michael W. King. [START_REF] King | Glycogen, Starch and Sucrose Synthesis[END_REF] In the muscle, glucose 1 Phosphate (GLc-1P) is utilized via glycogenolysis to generate metabolic energy. In the liver, GLc1-P is mostly converted by phospho-glucomutase and glucose-6-phosphatase to glucose, which is released for the benefit of other tissues, especially the central nervous system that relies on glucose as its major source of fuel. GP is an allosteric enzyme, which exists in three different isoforms in the liver, the muscle and the brain. GP is a homodimer, composed by two identical subunits of MW 97.5 kD each, that also contain the co-factor PLP. Figure 1.6 [START_REF] Barford | The Allosteric Transition of Glycogen-Phosphorylase[END_REF][START_REF] Barford | The Molecular Mechanism for the Tetrameric Association of Glycogen-Phosphorylase Promoted by Protein-Phosphorylation[END_REF]. Following the Monod-Wyman-Changeux allosteric model [START_REF] Monod | On Nature of Allosteric Transitions -a Plausible Model[END_REF], GP exists in two conformational states: T (low activity and affinity for substrates) and R (high activity and affinity for substrates). The equilibrium between the two states can be altered by the presence of allosteric modulators that stabilize one form or the other, regulating thus the activity. Further to this, GP activity is further regulated by the phosphorylation of Ser 14 by PhK, which transforms the inactive GPb to active GPa. The structure of T and R state of GPa and GPb have been determined by X-ray crystallography [START_REF] Barford | The Allosteric Transition of Glycogen-Phosphorylase[END_REF]). In the muscle enzyme, conversion from the T to R state by phosphorylation of AMP, induces a major quaternary structural change which results to a rotation of each subunits by 5 o about an axis at the dimer interface. In the T-state enzyme there is no access to the catalytic site from the surface since it is blocked by a loop termed 280s (residues 282 to 286). On transition from T-state to R-state, the 280s loop becomes disordered and displaced, thus opening a channel that allows access for glycogen to reach the active site from the surface (Figure1.6).

Signaling pathway

Phosphorylase kinase (PhK) enzyme integrates hormonal and neuronal signals and is a key enzyme in the control of glycogen metabolism. Figure 1.7 shows the major signaling pathways that converge at PhK activation. Adrenaline signaling through its seven transmembrane helix receptor activates the heterotrimeric G protein complex located on the inside of the plasma membrane, which, in turn, activates adenyl cyclase to produce the second messenger cyclicAMP (cyAMP). cyAMP activates cyclic AMP-dependent protein kinase (cAPK), which phosphorylates the alpha and beta subunits of PhK (approximately seven sites on the alpha subunit and four sites on the beta subunit [START_REF] Heilmeyer | Molecular-Basis Of Signal Integration In Phosphorylase-Kinase[END_REF]). The heterotrimeric G protein complex also activates phospholipase to release inositol triphosphate (IP3) from the plasma membrane. IP3 stimulates transient release of Ca 2+ from the endoplasmic reticulum.

Ca 2+ release from the sarcoplasmic reticulum is stimulated by a nerve impulse at the neuromuscular junction leading to depolarization of the plasma membrane (V). The depolarization signal is conveyed in the region of the T tubule invaginations via conformational changes in the dihydropyridine and ryanodine receptors to the sarcoplasmic reticulum to stimulate release of Ca 2+ . Ca 2+ binds to the calmodulin () subunit of PhK. ADP, produced by metabolism of ATP, binds to the subunit of PhK. The combined effects of phosphorylation, Ca 2+ binding, and metabolic stimulation activate PhK, which, in turn, phosphorylates glycogen phosphorylase (GP), converting inactive GPb to active GPa and the stimulating the glycogen breakdown.

Activation of PhK and catalytic mechanisms

The activation of phosphorylase kinase is a complex process with several tiers of control, in vivo its activation is regulated by phosphorylation on the alpha and beta subunits, ADP, Ca 2+ see chapter 1.2.5 for more details. PhK is one of several kinases that do not require phosphorylation on the kinase activation segment for activity. When isolated, the kinase domain is constitutively active. It has a glutamate in place of the more typical phosphorylatable serine, threonine, or tyrosine in the activation segment [START_REF] Lowe | The Crystal Structure of a Phosphorylase Kinase Peptide Substrate Complex: Kinase Substrate Recognition[END_REF][START_REF] Owen | Two Structures Of The Catalytic Domain Of Phosphorylase-Kinase -An Active Protein-Kinase Complexed With Substrate-Analog And Product[END_REF]. Hence, the role of the regulatory , and  subunits is to restrain, either directly or indirectly, the activity of the kinase domain until signals are received that relieve the inhibition (see below 1.2.5 for role of the various subunits).

In vitro, pH is a further factor that regulates activity. With inactive PhK isolated from muscle and assayed in the presence of Ca 2+ , the activity at pH 6.8 is low but increases sharply at pH 8.2. The ratio of activity (pH 6.8/pH 8.2) for non-activated PhK is < 0.05. Activity remains Ca 2+ dependent at pH 8.2. Activation of PhK by phosphorylation or limited proteolysis results in an increase in activity at pH 6.8 with little increase in activity at pH 8.2 and a resultant pH6.8/8.2-activity ratio of between 0.2 and 0.9 [START_REF] Brushia | Phosphorylase Kinase: The Complexity of Its Regulation Is Reflected in the Complexity of Its Structure[END_REF]. In this work we have focused on the structure of PhK in the inactivated state (pH 6.8 0.2 mM EDTA).

A catalytic mechanism of PhK has been proposed [START_REF] Skamnaki | Catalytic Mechanism of Phosphorylase Kinase Probed by Mutational Studies[END_REF] according to which phosphorylation proceeds with rapid equilibrium binding of substrates (random kinetic mechanism) the chemical step is fast and the rate-limiting step is the dissociation of products and ADP release. The individual microscopic rate constants were estimated by viscosity kinetic measurements. The contributions to catalysis of conserved amino acids such the catalytic Asp149 have been probed by mutational studies in combination to crystallographic evidence [START_REF] Lowe | The Crystal Structure of a Phosphorylase Kinase Peptide Substrate Complex: Kinase Substrate Recognition[END_REF]. The chemical step is consistent with the direct in line phosphoryl transfer supporting the notion of an associative mechanism as the transition state involves a pentavallent phosphorane where the apical bonds are weak and long.

The four subunits of PhK and their respective role

PhK attracted the scientific interest for decades. It was the first protein kinase to be discovered in 1955 by Fisher and Krebs [START_REF] Fischer | Conversion of Phosphorylase B to Phosphorylase a in Muscle Extracts[END_REF]. However, the structural basis of its regulation is still poorly understood. Phosphorylase kinase (PhK), total MW 1.3 x 10 6 , is one of the most complex kinases identified. It comprises a hexadecameric assembly of four different subunits arranged as an ()4 tetramer.

The (MW 138,000; 1237 amino acids) and  (MW 125,000; 1092 amino acids) subunits are regulatory inhibitory subunits. Analysis of the sequence of α and β subunits shows that they share more than 30% identity to sequences, they would therefore probably derived from a gene duplication [START_REF] Brushia | Phosphorylase Kinase: The Complexity of Its Regulation Is Reflected in the Complexity of Its Structure[END_REF].

Their phosphorylation by cyclic AMP-dependent kinase (cAPK) in response to hormonal stimulation leads to activation of the kinase. ADP binds to the  subunit with high affinity and also stimulates activity. While it is established that phosphorylation of the  subunit (and to a lesser extent the  subunits) gives rise to activation (Johnson 2009b), the mechanism of conformational changes produced by this posttranslational modification are not understood. The regulatory subunits appear to be inhibitory in that they restrain, either directly or indirectly, the activity of the kinase domain. Partial proteolysis of the  and  subunits, which leaves the and  subunits untouched, causes a large activation. In addition to the effects mediated by the endogenous  subunit, PhK can also be activated in a calciumdependent manner by exogenous calmodulin.

Neuronal stimulation is effected by Ca 2+ ions that bind to the kinase's intrinsic calmodulin subunit () (MW 16,700; 148 amino acids), thus coupling muscle contraction with energy production.

The  subunit (MW 44,700; 386 amino acids) is the catalytic subunit. It is composed of a kinase domain (residues 20-296) (Figure 1.3) and a regulatory auto-inhibitory domain (residues 298-386). The PhK  subunit catalytic domain has the classical bilobal protein kinase structure. This canonical architecture is characterized just like any other kinases (figure 1.3) by a smaller N-terminal lobe (residues 14-107), formed principally from  sheet, is connected to a larger C-terminal lobe (residues 110-292), formed mainly from helices. The two lobes are joined by a hinge region around residues Lys108 and Gly109. Within the regulatory domain are two sequences (residues 302-326 and 342-366) that bind tightly to calmodulin [START_REF] Dasgupta | Characterization of the Regulatory Domain of the Gamma-Subunit of Phosphorylase-Kinase -the 2 Noncontiguous Calmodulin-Binding Subdomains Are Also Autoinhibitory[END_REF][START_REF] Dasgupta | The Gamma-Subunit Of Skeletal-Muscle Phosphorylase-Kinase Contains 2 Noncontiguous Domains That Act In Concert To Bind Calmodulin[END_REF] .

Previous structural studies on PhK

In order to understand the structural basis of the mechanisms involved in control of PhK through protein-protein interactions and by phosphorylation, a structure of the whole enzyme is required. However, structural studies on PhK were hindered by the enzyme's complexity. Intact PhK has been difficult to crystallize for X-ray studies, but it has proved amenable to single-particle analysis by electron microscopy.

3D models from negatively stained PhK

Vé nien-Bryan and co-workers previously determined the three dimensional (3D) structure of PhK pH8.2 activated at 22Å resolution using negative stain electron microscopy and the random conical tilt method [START_REF] Vé Nien-Bryan | Three-Dimensional Structure of Phosphorylase Kinase at 22 A Resolution and Its Complex with Glycogen Phosphorylase B[END_REF]. The 2D classification shown three major classes: the dominant butterfly form consisting of two wings, the chalice form consisting of a cup and a stem and the tetrad form characterized by a rectangular shape structure with a stain filled central cavity (Figure 1.8). 3D reconstructions by the random conical tilt method followed by refinement revealed a structure exhibiting 222 symmetry consisting of two opposing lobes held together by two short oblique cross bridges The dimensions of the lobes in the projection are 225 x 110Å, and the bridges are 55Å in length and 45Å in width. The dimensions of the overall structure are 270 x 225 x 160 Å. Figure1.9

shows the butterfly and the chalice and the tetrad orientations (rotations of 45 o and 90 o respectively, about the horizontal axis of the butterfly orientation). The hole inside the tetrad structure has a cross-section of 35 x 85 Å. The relative orientation of the lobes can be seen after a 90 o about the vertical axis of the butterfly orientation (Figure 1.9D). One lobe is rotated 65 o relative to the other. This 3D model has been used as model for further image analysis in this study and will be referred as "model 1" in this work. In the same paper, the structure of PhK decorated with GPb in the presence of activating Ca 2+ , Mg 2+ at pH 8.2 was also published. The 28 Å resolution 3D structure shown that GPb bound toward the end of the lobes.

At the same time, [START_REF] Nadeau | A Ca2+-Dependent Global Conformational Change in the 3D Structure of Phosphorylase Kinase Obtained from Electron Microscopy[END_REF] compared the negatively stained structures of inactive PhK at pH 6.8 in the absence and presence of calcium (Figure 1.10).

The structure is characterized by the same features as the model described above (inactive pH8.2) i.e. a butterfly-like structure and two-bridges connecting the lobes however, the presence of calcium promotes a redistribution of density throughout the lobe and bridge regions without perturbing the overall dimensions. It is also interesting to note that the angle between the lobes is again about 65 o as reported in the previous model (Figure 1.10, row 2). This Nadeau model was further elaborated in subsequent small angle X-ray scattering to detect calcium induced structural changes [START_REF] Priddy | Ca2+-Induced Structural Changes in Phosphorylase Kinase Detected by Small-Angle X-Ray Scattering[END_REF].

3D models from frozen PhK

The better resolved structure of 9.9Å from cryo specimen (18,123 particles) Calcium activated at pH8. previously mentioned structures (Figure1.11). The overall dimensions were 270x225x160Å, the dimensions of the lobes in the projection are 225x110Å, while the bridge length are 55Å and width 45Å. The angle between the lobes is 65 o as observed previously [START_REF] Vé Nien-Bryan | Three-Dimensional Structure of Phosphorylase Kinase at 22 A Resolution and Its Complex with Glycogen Phosphorylase B[END_REF], Nadeau et al., 2002). However this resolution was not high enough to resolve secondary structures such as alpha helices.

Another model of PhK at 25Å resolution using 4878 frozen particles cryo specimen but in the inactive state was published in 2005 by our collaborators on this project [START_REF] Nadeau | Cryoelectron Microscopy Reveals New Features in the Three-Dimensional Structure of Phosphorylase Kinase[END_REF] . The broad features noted in this lower-resolution inactivated PhK (25Å, 4878 particles) are consistent with the higher-resolution (9.9Å, 18,123 particles) PhK Calcium activated at pH8.2 model. But there are two main differences. The differences refer to i) the angles between the lobes which was reported 90 o in the lower-resolution PhK inactivated instead of 65 o in the higher-resolution PhK and also in all the previous 3D models published (see chapter 1.2.6.1) and ii) the surprising observation that the two bridges were resolved to four bridges (Figure 1.12) with asymmetric connections to the two lobes in this inactivated form These differences between the two cryoimages give intriguing clues to the possible conformational changes that occur on activation of PhK that will be the subject of a future comparison in this present work. In our work, we used the low-resolution PhK inactivated 3D model as "model 2" for our image analysis. However it's important to note that a characteristic of this model is that a low threshold value needs to be used in order to visualize the bridges between the two lobes; a more standard value of threshold do not allow to see any links between the two lobes.

Location of the subunits onto the complex

Several cross-linking, immuno-electron microscopy and biochemical studies contributed to the identification of the likely position of individual subunits. In the butterfly structure the tetramers () associate in a head to tail fashion with ()2 comprising each lobe Figure 1.13 .

The alpha subunit

The  subunit is the most susceptible of the four subunits to proteolysis by a number of proteases [START_REF] Trempe | Detection of Conformational-Changes in Phosphorylase-Kinase with Proteases[END_REF]. It is presumed to be largely surface exposed and it is likely that the exterior lobes are largely composed of  subunits. This is supported by two findings. (i) The location of epitopes for monoclonal antibodies against the α subunit (amino residues 1132-1237) situated at the top of the lobes of PhK [START_REF] Wilkinson | An Epitope Proximal to the Carboxyl-Terminus of the Alpha-Subunit Is Located near the Lobe Tips of the Phosphorylase-Kinase Hexadecamer[END_REF]

. (ii)

Cross-linking experiments using transglutaminase have shown that small amounts of  dimers are formed with activated PhK that are not observed with non-activated PhK [START_REF] Nadeau | Zero Length Conformation-Dependent Cross-Linking of Phosphorylase-Kinase Subunits by Transglutaminase[END_REF]. This finding suggests that the polypeptide backbones of the -subunits stretch from the lobe tip to a more central location where they abut. Interactions between and other subunits exist. The N-terminal ~500 residues of the  subunit contains a region that interacts with the  subunit [START_REF] Nadeau | Zero-Length Crosslinking of the Beta Subunit of Phosphorylase Kinase to the N-Terminal Half of Its Regulatory Alpha Subunit[END_REF] and an interaction between residues 724 and 981 of the  subunit and the C-terminal domain of the  subunit has also been demonstrated [START_REF] Harris | Purification and Characterization of Catalytic Fragments of Phosphorylase-Kinase Gamma-Subunit Missing a Calmodulin-Binding Domain[END_REF]).

The beta subunit

The important role for the  subunit in forming the quaternary structure of PhK is supported by various observations. (i) An epitope in the subunit (residues 708-815) is localized to an interior position on the lobes near the interconnecting cross-bridges [START_REF] Wilkinson | Proximal Regions of the Catalytic Gamma and Regulatory Beta Subunits on the Interior Lobe Face of Phosphorylase Kinase Are Structurally Coupled to Each Other and with Enzyme Activation[END_REF]. (ii) Selective proteolysis of the  subunit does not abolish bridges, whereas proteolysis of both  and  leads to the formation of unbridged single lobes [START_REF] Trempe | Detection of Conformational-Changes in Phosphorylase-Kinase with Proteases[END_REF] . (iii) An  trimer has been produced by LiBr-mediated precipitation of the  subunit from the holoenzyme (Pickett-Gies and Walsh 1985; [START_REF] Wilkinson | Proximal Regions of the Catalytic Gamma and Regulatory Beta Subunits on the Interior Lobe Face of Phosphorylase Kinase Are Structurally Coupled to Each Other and with Enzyme Activation[END_REF].

The  complex is devoid of bridge elements as seen on electron microscope micrographs [START_REF] Trempe | Detection of Conformational-Changes in Phosphorylase-Kinase with Proteases[END_REF]. (iv) There are two bridges but four  subunits, suggesting the occurrence of  dimerization in an interlobal bridging; in support of this notion, crosslinking of the nonactivated holoenzyme with a short cross-linker results in the formation of a  dimer [START_REF] Ayers | Effector-Sensitive Cross-Linking of Phosphorylase B Kinase by the Novel Cross-Linker 4-Phenyl-1,2,4-Triazoline-3,5-Dione[END_REF]. (v) Cross-linking experiments have shown that Lys303 from the  subunit can form cross-links with Arg18 in the N-terminal region of the  (O. Nadeau et al. 2007) Recently by circular dichroism studies, the secondary structure of  subunit has been determined to be mostly helical while by using an multidisciplinary approach involving chemical cross linking, mass spectrometry, threading and ab initio calculations, an atomic model of the tertiary structure of  subunit was constructed (O. W. Nadeau et al. 2007). The model was docked to the central bridge region of PhK cryo-EM structure in support of the notion of  subunits act as scaffold, positioning the lobes, upon which the other subunits are arrayed. (Nadeau et al., 2007).

The gamma subunit

Only the truncated catalytic domain of the gamma subunit (PhKt amino acids 1-299) is known at high resolution [START_REF] Owen | Two Structures Of The Catalytic Domain Of Phosphorylase-Kinase -An Active Protein-Kinase Complexed With Substrate-Analog And Product[END_REF][START_REF] Lowe | The Crystal Structure of a Phosphorylase Kinase Peptide Substrate Complex: Kinase Substrate Recognition[END_REF]. The  subunit is located at the interior of the lobes as indicated by antibody and further supported molecular fitting of the high resolution structure of its N-terminal (aa 1-299) catalytic domain PhKt to the 10Å structure [START_REF] Vé Nien-Bryan | The Structure of Phosphorylase Kinase Holoenzyme at 9.9 Å Resolution and Location of the Catalytic Subunit and the Substrate Glycogen Phosphorylase[END_REF]).

Although there is currently no direct structural information for the C-terminal component of the subunit (amino acids 298-396) the molecular fitting showed it is oriented towards the bridge. This C-terminal region of the  subunit acts as an autoinhibitor of catalytic activity. In the presence of calcium, it is proposed that the interaction of the C-terminal region with the calmodulin--subunit Ca 2+ sensor serves to relieve inhibition. The interaction most likely involves the PhK calmodulin-binding region residues 343-366 (termed PhK5) [START_REF] Cook | Structural Characterization of Ca2+/CaM in Complex with the Phosphorylase Kinase PhK5 Peptide[END_REF][START_REF] Dasgupta | Characterization of the Regulatory Domain of the Gamma-Subunit of Phosphorylase-Kinase -the 2 Noncontiguous Calmodulin-Binding Subdomains Are Also Autoinhibitory[END_REF][START_REF] Dasgupta | The Gamma-Subunit Of Skeletal-Muscle Phosphorylase-Kinase Contains 2 Noncontiguous Domains That Act In Concert To Bind Calmodulin[END_REF] and possibly the less conserved residues 302-326 (PhK13).

The delta calmodulin subunit

Nanogold labeling studies have shown that the  subunit is located very close to the bridge [START_REF] Traxler | Direct Visualization of the Calmodulin Subunit of Phosphorylase Kinase via Electron Microscopy Following Subunit Exchange[END_REF]. The  subunit is shown as dark green figure 1.13.

It is reported that calcium perturbs the interaction of  with the catalytic subunit suggesting that the flow of structural information from subunit to  is mediated by the Cterminal of the catalytic domain [START_REF] Rice | The Calmodulin-Binding Domain of the Catalytic Gamma Subunit of Phosphorylase Kinase Interacts with Its Inhibitory Subunit -Evidence for a Ca2+-Sensitive Network of Quaternary Interactions[END_REF].

As mentioned above, structural data at high resolution are available for the kinase domain, more precisely the truncated gamma catalytic domain (PhKt amino acids 1-299) [START_REF] Owen | Two Structures Of The Catalytic Domain Of Phosphorylase-Kinase -An Active Protein-Kinase Complexed With Substrate-Analog And Product[END_REF][START_REF] Lowe | The Crystal Structure of a Phosphorylase Kinase Peptide Substrate Complex: Kinase Substrate Recognition[END_REF]) and for the delta subunit which is a calmodulin, but little is known of the homologous  and subunits.

However, various bioinformatics studies [START_REF] Pallen | Glucoamylase-like Domains in the Alpha-and Beta-Subunits of Phosphorylase Kinase[END_REF][START_REF] Carriere | Calcineurin B-like Domains in the Large Regulatory Alpha/beta Subunits of Phosphorylase Kinase[END_REF][START_REF] Carriere | 3D Mapping of Glycogenosis-Causing Mutations in the Large Regulatory Alpha Subunit of Phosphorylase Kinase[END_REF][START_REF] Nadeau | Structure and Location of the Regulatory Beta Subunits in the (alpha Beta Gamma delta)(4) Phosphorylase Kinase Complex[END_REF] predict that both subunits contain structurally similar domains A,B,C,D to other known enzymes. Indeed, in both subunits domains A and B (N-terminal regions residues 1-436  subunit) or residues 40-477 ( subunit)) are identified to be similar with the  barrel glucoamylase like GH15 proteins whereas the C and D domains (C-terminal region residues 1066-1237 ( subunit) and 918-1093 ( subunit)) share similarities with the calcineurin B-like proteins (CBL domains). CBL domains are composed of two pairs of EF hands that might contribute to the regulation by Ca 2+ . In addition, a leucine zipper motif has been identified in the  subunit, but not the  subunit, for residues 833-854 [START_REF] Ayers | Self-Association of the Alpha Subunit of Phosphorylase Kinase as Determined by Two-Hybrid Screening[END_REF]. In a more recent study [START_REF] Nadeau | Structure and Location of the Regulatory Beta Subunits in the (alpha Beta Gamma delta)(4) Phosphorylase Kinase Complex[END_REF] for the  subunit, domains C and D are modelled as a single domain using not a CBL protein but a PP2AA template.

The importance of these domains in PhK is demonstrated by the finding that several mutations in these domains are related to X-linked liver glycogenosis.

Mutations in PhK and diseases

Medical target

The correlation of glycogen metabolism to many metabolic disorders has targeted GP and PhK, for the development of drugs, for a number of metabolic diseases, predominantly diabetes mellitus but also cancer. Pharmacological modulation of the glycogen metabolism and the consequences of inhibiting glycogenolysis are a continuing challenge for physiology, biochemistry and pharmacology. The number of drug related patents filled by pharmaceutical and biotechnology companies for these two enzymes is steadily increasing in the last years [START_REF] Skamnaki | Glycogen: Structure, Functions in the Body and Role in Disease[END_REF].

Malfunctions of glycogen metabolism enzymes are responsible for pathologies (more or less severe) such as type 2 diabetes, which are deficiencies related to glycogen storage called "Glycogen Storage Disease, GSD". Twelve GSD types have been identified. The type IX affects a strategic point of regulation of glycogen breakdown by affecting Phosphorylase Kinase. The GSD type IX cover 25% of cases of GSD with a frequency of 1/100 000 births [START_REF] Hendrickx | Genetic Deficiencies of the Glycogen Phosphorylase System[END_REF]. Such diseases may be related to the absence of expression of the gene encoding the enzyme, the expression of a mutated gene leading to an imperfect shape of the protein or in expression of a mutated gene leading to a complete and folded protein but inactive from a functional point of view. There are various kinds of mutations, some corresponding to the insertion or deletion of one or more nucleotides in the DNA sequence, so often causing a reading frame shift sequence or early cessation of the translation of mRNA into protein. Other types of mutations are associated with substitutions of one nucleotide by another. The substitutions in a gene can be silent due to the redundancy of the genetic code (different triplets encoding the same amino acid), the individual is then healthy. But when substitution in a gene is not silent, the corresponding protein sequence differs from the "normal" protein, it may result in the occurrence of diseases. Such mutations are called missense. In the case of a patients suffering from GSD type IX bound to the PhK (PhK deficiency), clinical signs are multiple, often exhibits hepatosplenomegaly (where both the liver and spleen swell beyond their normal size), delayed growth or muscle weakness. The Blood tests reveal hypoglycemia, and hyperlactacemia and hyperlipidemia. Urine analysis shows that ketone compounds and urea are present at too high concentrations. Severe cases of the disease are associated with cirrhosis and can be fatal. This multiplicity of clinical signs is a consequence of the complexity of PhK, of the biochemical composition (α,β,γ,δ)4 with 4 different isoforms depending on the biological tissue. Thus, the α and γ subunits have two major isoforms in the muscle and liver, encoded by two distinct genes. The β subunits and δ are ubiquitous and are therefore devoid of tissue specific isoforms. The two major isoforms of PhK allow to distinguish between two types of GSD, i) specific for the liver (causing glycogen storage diseases) ii) specific to the muscle (causing myopathies).

The liver specific glycogen storage diseases are caused by mutations affecting genes encoding the liver isoform α subunits (PHKA2), β subunit (PHKB) and γ subunit (PHKG2). Specific glycogenoses of the muscle are due to mutations in the genes encoding the muscle isoform subunits α (PHKA1), β subunit (PHKB), and γ subunit (PHKG1). Therefore, mutations the α and β subunits in humans causes a sudden drop in PhK activity. Studies are continuing to improve the therapeutic diagnosis and drug design.

Goal of this study

The activation of phosphorylase by phosphorylation is the end result of one of the best understood signaling pathways (Figure 1.14A). As a result of numerous structural studies over the years, we now understand the metabolic transformations and the structures of the players involved in this pathway (Figure 1.14B shows all the protein structure solved in this pathways). There are two major gaps in our understanding of the structural basis of this pathway. First, how an activated GPCR (G-protein-coupled receptor) in turn binds and activates a heterotrimeric G-protein and, secondly, how phosphorylation of PhK by PKA activates this very large kinase complex (Figure 1.14B). There has been progress in understanding the structure of PhK (1.3×10 6 Da) from three-dimensional image reconstruction of cryo-electron micrographs [START_REF] Vé Nien-Bryan | The Structure of Phosphorylase Kinase Holoenzyme at 9.9 Å Resolution and Location of the Catalytic Subunit and the Substrate Glycogen Phosphorylase[END_REF]) and the X-ray structure of the catalytic γ subunit [START_REF] Owen | Two Structures Of The Catalytic Domain Of Phosphorylase-Kinase -An Active Protein-Kinase Complexed With Substrate-Analog And Product[END_REF][START_REF] Lowe | The Crystal Structure of a Phosphorylase Kinase Peptide Substrate Complex: Kinase Substrate Recognition[END_REF]), but how the large complex of α and β subunits together with a calmodulin domain act to restrain the kinase in the inactive state until stimulation remains a subject of interrogation. In this thesis work, I built on previous work and elaborate on new structural work involving the PhK in the inactivated state in order to gain some structural information in the activation process of PhK.

The revolution in the use of the electron cryo-microscopy

During my thesis work, I have investigated the structure of two protein complexes, the FANCD2/FANCI complex and the Phosphorylase Kinase. Both complexes were studied Three-dimensional structures of each of the steps of the pathway. The PDB codes are: β2adrenergic receptor, 2RH1; heterotrimeric G-protein, 1GP2; adenylate cyclase-Gα complex, 1AZS; PKA-regulatory subunit complex, 2QCS; PKA active, 1ATP; PhK catalytic subunit, 2PHK; PhK holoenzyme (EM-PDB1527); GPb, 1A81; GPa, 1GPA. From [START_REF] Johnson | a Protein Kinase Inhibitors: Contributions from Structure to Clinical Compounds[END_REF] using cryo electron microscopy combined with image analysis. However, the former project (FANCD2/FANCI complex) was performed using conventional electron microscope and the images were taken using CCD camera. The PhK project was performed using state of the art electron microscope and direct electron detection camera. I therefore experimented both methods, before and after the "revolution". And as clearly described Wener Kuhlbrandt in its review, cryo-EM enters a new era [START_REF] Kühlbrandt | Cryo-EM Enters a New Era[END_REF].

Advances in detector hardware and image-processing software have led to a revolution in the use of electron cryo-microscopy to determine complex molecular structures at high resolution.

The main challenges in cryo electron microscopy

The two main problems, which were hampering the cryo electron microscopy combined to image analysis to bring information at high resolution, were:

-The low signal-to-noise ratio, the consequences being the very low contrasted images -The drift of the specimen inside the electron microscope the consequences being the degradation of the images through blurring One of the two main challenges in cryo-EM is the low signal-to-noise ratio or, in other words, poor image contrast. Low contrast is the inevitable consequence of the fact that all biological objects and their components: proteins, nucleic acids, carbohydrates, lipids are made of elements of low atomic mass (C, H, O, N) atoms which interact with electrons much less than large atoms with higher atomic mass) and are radiation-sensitive. In order to determine their structures in atomic detail, as is necessary to understand how they function in the cell, only very low electron doses can be applied. Exposures above a critical limit break covalent bonds irreversibly, destroying the very detail we want to investigate.

To make matters worse, the particles must not move by more than about 1 Å, which is equivalent to the diameter of one hydrogen atom, while an image is being recorded. However, much larger movements usually occur when the electron beam hits a biological specimen, possibly due to the release of stresses that result from the sample and the film that support it all having different thermal expansion coefficients. Electron irradiation breaks covalent bonds and creates gas molecules (such as hydrogen, oxygen, nitrogen and methane) that push through the ice to escape. These unavoidable beam-induced movements have made it extremely difficult to collect high-resolution electron micrographs of biological macromolecules in the past. It takes seconds to record a low-dose image on conventional media (CCD cameras or photographic film), during which time small movements build up and blur the high-resolution features.

"The revolution"

We can divide this revolution in two parts: the new direct electron detectors and the development of new software.

The new direct electron detectors are both more sensitive and much faster than conventional media. Instead of a single, second-long exposure, they record movies at a rate of many frames per second. Successive movie frames are compared in order to detect any movements on the Å scale. Once the movements of the particles have been traced, they can be reversed in the computer. The movie frames are then added up to give one much sharper, motion-corrected image.

In addition, to take full advantage of the new detector hardware, programs were developed to perform movie processing and motion correction [START_REF] Li | Electron Counting and Beam-Induced Motion Correction Enable near-Atomic-Resolution Single-Particle Cryo-EM[END_REF] and, above all, to extract the precious structural information from noisy images in an optimal way. A program suite based on maximum likelihood and prior knowledge (such as a reference volume) about the structure to be determined (Scheres 2012a) has taken the field by storm.

The new software works largely without user intervention and provides objective resolution criteria that have quickly become the 'gold standard' in cryo-EM. Standardized validation procedures make results more reliable [START_REF] Rosenthal | Optimal Determination of Particle Orientation, Absolute Hand, and Contrast Loss in Single-Particle Electron Cryomicroscopy[END_REF] and produce clearer maps by helping to avoid over-fitted noise that has bedeviled many of the previously published single-particle cryo-EM structures. In addition, powerful classification schemes separate different molecules [START_REF] Amunts | Structure of the Yeast Mitochondrial Large Ribosomal Subunit[END_REF].

Chapter II Materials And Methods

Samples Production

Preparation of PhK in this project

Phosphorylase kinase has proven difficult to characterize. The inability to break it down easily into its component parts has markedly hampered its investigation. Free soluble gamma subunit cannot be isolated following synthesis in bacterial, yeast, insect or mammalian expression systems and free soluble alpha or beta subunit cannot be isolated from synthesis in insect cells.

To our knowledge, despite numerous attempts no laboratory has succeeded to express the individual alpha and beta subunits or co-express the alpha and beta subunits in any expression systems. Donald Walsh and Gerry Carlson teams reported [START_REF] Kumar | Baculovirus-Mediated Overexpression of the Phosphorylase B Kinase Holoenzyme and Alpha Gamma Delta and Gamma Delta Subcomplexes[END_REF][START_REF] Boulatnikov | Expressed Phosphorylase B Kinase and Its Alpha Gamma Delta Subcomplex as Regulatory Models for the Rabbit Skeletal Muscle Holoenzyme[END_REF]) that recombinant baculovirus carrying a vector containing a multigene cassette was created to coexpress in insect cells α, β, γ, and δ subunits corresponding to rabbit skeletal muscle PhK. In this work, the hexadecameric recombinant PhK (rPhK) and its corresponding αγδ trimeric subcomplex were purified to homogeneity with proper subunit stoichiometries. However these PhKr were not produced at high quantity enough for structural studies.

Therefore, during this structural study we have imaged the PhK that our collaborators: Dr. Owen Nadeau and Prof. Gerald M. Carlson (Department of Biochemistry and Molecular Biology, University of Kansas, Medical Center) have purified from white rabbit following the method described in [START_REF] Cohen | Phosphorylase-Kinase from Rabbit Skeletal-Muscle[END_REF] . The specimen was in the inactivated state, in BGP buffer (50 mM 2-glycerophosphate, pH6.8, 0.2mM EDTA). PhK was diluted to the appropriate concentration for EM analyses and applied to the grids using the same buffer.

Purification of the Phosphorylase kinase

Nonactivated PhK from fast-twitch skeletal muscle of female New Zealand White rabbits was purified as described through the DEAE-cellulose step and stored frozen in 50 mM HEPES (pH 6.8), 0.2 mM EDTA, 10% (w/v) sucrose. The complex used for electron microscopy was thawed and additionally purified immediately prior to use by gel filtration, in order to remove any aggregated material, with an analytical Superdex 200 column in buffer H (50 mM HEPES [pH 8.2], 200 mM NaCl, 2% [w/v] sucrose). This step resulted in considerable improvement of the appearance of the material in the electron microscope [START_REF] Vé Nien-Bryan | Three-Dimensional Structure of Phosphorylase Kinase at 22 A Resolution and Its Complex with Glycogen Phosphorylase B[END_REF], giving a homogeneous preparation.

FANCD2/FANCI complex

The expression and purification of the FANCD2/FANCI complex was perfomed by our collaborator Dr. Martin Cohn and its team. Cell lines, antibodies, and plasmids: HeLa and PD20 cells were grown in DMEM (D5796, Sigma) supplemented with 2.5-10% FBS.

Antibodies used were as follows: anti-FANCD2 (sc-20022, Santa Cruz Biotechnology); anti-FANCI (FARF); and anti-α-tubulin (5829, Millipore). EGFP-fused FANCD2 cDNA was expressed using the pOZ-N plasmid as described (Liang et al., 2015). shRNA-mediated knockdown of the FANCD2 gene was achieved by expressing the target sequence 5'-GAGCAAAGCCACTGAGGTA-3' in the pSuper.retro vector (Clontech). Transfections of plasmid DNA were carried out using FuGENE6 (Promega) according to the manufacturer's instructions. The FANCD2 tower domain deletion plasmid was generated by deleting amino acids 1,147-1,451.

Protein purification: FANCD2 is expressed using the pFastBac1 vector (Life Technologies) with an engineered N-terminal Flag-HA tag, and FANCL is expressed using the pFastBac1 vector (Life Technologies) with an engineered N-terminal Flag-MBP tag. For FANCD2/FANCI complex, Sf9 cell pellets were resuspended in lysis buffer (20 mM Tris-HCl pH 8.0, 0.1 M KCl, 10% glycerol and 0.2 mM PMSF), and sonicated. Lysates were clarified by centrifugation, and the supernatants were incubated with M2 anti-FLAG agarose resin (A2220, Sigma) for 2 hr. The resin was washed carefully, and the protein was eluted in the same buffer containing 0.5 mg/ml FLAG peptide. The flag eluate was supplemented with 20 mM Imidazole and incubated with Ni 2+ -NTA (30310, QIAGEN) at 4 degree for 2 hour with rotation. The resin was washed carefully, and eluted with buffer containing 20 mM Tris (pH 8.0), 0.1M KCl, 250 mM Imidazole, 0.2 mM PMSF and 10% glycerol. The Ni 2+ -NTA eluate was injected into pre-equilibrated size exclusion chromatography column, Superdex 200, and eluted with the base buffer containing 20 mM Tris (pH 8.0), 0.1M KCl and 5% glycerol. The FANCD2-ΔTower/FANCI complex is purified as full-length FANCD2/FANCI except the size exclusion chromatography step. UBA1 and FANCL are purified against FLAG tag described as above.

Transmission Electron Microscopy -TEM

The electron microscope consists of an electron gun (focalization system, electron source, electron accelerator), a vacuum system, a system of electromagnetic lenses and apertures, a specimen holder with a nitrogen cooling system, and a display and recording system of the image formed. The assembly is under vacuum so that the only possible interaction is between electrons and the sample. Transmission electron microscopy uses a high-energy electron beam (subjected to high acceleration voltages of 60 to 300 keV) to bombard the sample (thin object from 10 to 150 nm).

Negative Staining

The negative staining method [START_REF] Brenner | A Negative Staining Method for High Resolution Electron Microscopy of Viruses[END_REF] is used to obtain negative images of macromolecules with very high contrast. Biological sample is coated into a mold of heavy atoms from uranium, tungsten or molybdenum deposited on carbon-coated grid [START_REF] Harris | Negative Staining and Cryoelectron Microscopy: The Thin Film Techniques[END_REF][START_REF] Valentine | Regulation of Glutamine Synthetase. XII. Electron Microscopy of the Enzyme from Escherichia Coli[END_REF].

Heavy ions are used because they interact very easily with the electron beam. The deflection of the electron beam is much higher in the stain regions than that in the stainprotein-stain region, as highly deflected electrons are filtered out, most electrons passing through the protein are conserved, then phase contrast of the protein can be produced. This technique is fast to implement, but also induces artifacts: flattening of structures [START_REF] Bibliographie Adrian | Cryo-Electron Microscopy of Viruses[END_REF]) loss of certain proteins [START_REF] Olkkonen | The Nucleocapsid of the Lipid-Containing Double-Stranded RNA Bacteriophage Phi 6 Contains a Protein Skeleton Consisting of a Single Polypeptide Species[END_REF]. In addition, as the stain does not penetrate inside the sample, so the contrast presents only the stain covering the surface of the protein. The stain is also uneven around the protein [START_REF] Mannella | Negative Staining Characteristics of Arrays of Mitochondrial Pore Protein: Use of Correspondence Analysis to Classify Different Staining Patterns[END_REF]; some regions may have more stain than other regions and result unreal contrast change.

Cryo-EM

Cryo-electron microscopy (cryo-EM) method [START_REF] Taylor | Electron Microscopy of Frozen Hydrated Biological Specimens[END_REF][START_REF] Dubochet | Electron Microscopy of Frozen Water and Aqueous Solutions[END_REF][START_REF] Lepault | Electron Microscopy of Frozen Biological Suspensions[END_REF][START_REF] Bibliographie Adrian | Cryo-Electron Microscopy of Viruses[END_REF]) is more difficult to prepare than negative staining method but can prevent the native structure of fully hydrated macromolecules from several influences of negative staining method. As by plunging a prepared grid into liquid ethane at -180°C at a very high speed, the specimen is frozen and maintained in a biologically interesting hydrated state, and water will be transformed into Liquid nitrogen is at a cold enough temperature (-195°C) to speed the process.

Unfortunately, its heat capacity is very low. Therefore, as soon as a grid at room temperature is dropped into the liquid nitrogen, some of it will warm and boil off, slowing the freezing process and allowing cubic ice to form.

Ethane has a much higher heat capacity than liquid nitrogen and is also liquid at temperatures just slightly above those of liquid nitrogen. Therefore, liquid ethane is cold enough (it's melting point is -188°C) to freeze water quickly and correctly, while not boiling off in the process. [START_REF] Stewart | Electron Microscopy of Biological Macromolecules[END_REF] To prepare the grid, a small vial of ethane is placed inside a larger liquid nitrogen reservoir. An EM grid is held in place at the bottom of a plunger. The plunger has a heavy weight at the top for extra force. Once the ethane in the vial is completely frozen, it needs to Schematic diagram of a freezeplunger [START_REF] Stewart | Electron Microscopy of Biological Macromolecules[END_REF] Cryoplunge TM 3 for the preparation of frozen hydrated specimens for cryo-EM Gatan.com A piece of filter paper is then pressed against the sample to blot of the excess buffer.

After a predetermined time, the filter paper is removed, and the plunger is allowed to drop into the liquid ethane. Once the grid enters the liquid ethane, the sample is rapidly frozen, and the grid can then be moved to a storage box in liquid nitrogen for later use in the microscope.

Grid

Most biological EM work is done on small (several millimeters) copper discs called grids cast with a fine mesh. This mesh can vary a lot depending on the intended application, but is usually about 15 squares per millimeter. On top of this grid, a thin layer of carbon is deposited by evaporation carbon graphite onto it. It is on this thin carbon film that the sample will then rest so that is can be examined in the microscope.

Carbon is generally a hydrophobic substance. To make the surface more accessible to water and the suspended sample, the carbon needs to be made hydrophilic. This is accomplished by glow discharging, giving the carbon film an overall hydrophilic surface.

Holey Carbon Film Grid

Although the carbon film is very thin and usually presents no big problem in obscuring a sample, for high-resolution studies of unstained biological macromolecules any interference on the beam can be problematic. For high-resolution studies, holey grids are used in grids are covered with a fine layer of carbon. However, as part of the preparation process, the carbon film is deposited in such a way that there are holes of a desired size in the carbon. One of the purposes of these holes is to eliminate any absorption and scattering of the electron beam by the carbon film, which will generate noise and obstruct the signal. Because cryo EM does not use staining, any elimination of background noise is desirable. The holes also allow for "pockets" of solvent to form. Within these pockets, the specimen remains fully hydrated, even when the sample has been frozen.

A problem can arise when holey grids are used in conjunction with proteins that are positively charged, such as DNA binding proteins. Because carbon film has a slight negative charge, especially after glow discharging, it readily attracts positively charged proteins.

Therefore, such a protein will preferentially land on the carbon as opposed to the holes.

A workaround has been developed using a secondary carbon layer. A holey grid is made as described above. Before depositing the sample, however, a very thin layer of carbon is deposited above the original film. Because this carbon is continuous, there is no preference to where the protein will lay down. Furthermore, the layer is thin enough to not have a great impact on the signal to noise ratio of the sample. In our study we have used quantifoil grids.

Advantages of cryo-EM over negative staining

The sample is always in solution and never comes into contact with an adhering surface. Therefore, the shape that is observed is the true shape of the hydrated molecule in solution and has not been distorted by attaching itself and flattening against the supporting film.

There is no stain to distort the sample. Stain does not always lay down evenly, which can generate artifacts and false contrasts when reconstructing the structure of a sample. Also, the staining process requires that the sample be blotted dry. During the drying, the sample can be damaged in many ways, such as by flattening and twisting.

When the sample adheres to the carbon grid, it could stick in a preferential orientation.

Then information of the calculated model in that direction will be lacking.

Low dose methods are normally used, and electron beam caused irradiation damage to the sample will be reduced.

As specimen is frozen in time, thermal vibrations are reduced to a minimum, and the corresponding noise is removed from the images. When a specimen is bombarded with electrons, some of these electrons are absorbed into the surrounding medium (vitreous ice in the case of a good cryo sample). There they energize the ice (also known as charging) and induce vibrations, which can move the embedded sample. This movement reduces the resolution that can be achieved and also damages the sample. To avoid this problem, it is useful to capture part of the holey carbon grid within the electron beam. At high enough voltages, carbon can conduct charges over its surface. When the electron beam charges the ice, the carbon can act as a ground sink, bleeding surface electrons away from the delicate sample.

Disadvantages of cryo-EM

The signal to noise ratio is very low. Biological macromolecules are normally made up of carbon, hydrogen, oxygen, and nitrogen. The electron absorption of such molecules is very low. As a result, image contrast is also very low and it is hard to detect features when dealing with just a few images.

If vitreous ice cannot be easily formed, the resulting cubic ice absorbs electrons very easily and the frozen sample is basically worthless (Figure 2.05). [START_REF] Dubochet | Electron Microscopy of Frozen Water and Aqueous Solutions[END_REF])

Images Formation

Image formation in electron microscopy depends on the nature of electron beam, particles nature and wave nature. Interaction between electron and matter cause amplitude contrast and the wave nature introduces phase lag, and produces phase contrast.

Contrast Transfer Function -CTF

The contrast transfer theory is intended to describe quantitatively the relationship between the wave function at the exit of the specimen and the intensity of the final image [START_REF] Frank | Three-Dimensional Electron Microscopy of Macromolecular Assemblies[END_REF]. The Fourier transform of the image (micrograph) of the object is equal to the Fourier transform of the object (specimen) multiplied by the contrast transfer function.

Then the contrast transfer function is described like [START_REF] Zhu | Three-Dimensional Reconstruction with Contrast Transfer Function Correction from Energy-Filtered Cryoelectron Micrographs: Procedure and Application to the 70SEscherichia coliRibosome[END_REF]:

𝐶(𝑘) = 2 [sin 𝛾(𝑘) -𝑊 cos 𝛾(𝑘)]
Where W represents the amplitude contrast ratio, k is the spatial frequency, γ(k) is the phase displacement depending on: defocus value applied, spherical aberration, and wave length.

Or simplified like [START_REF] Frank | Three-Dimensional Electron Microscopy of Macromolecular Assemblies[END_REF]:

𝐻 ′ (𝑘) = sin 𝛾(𝑘) -𝒬 0 cos 𝛾(𝑘)
The value of 𝒬 0 (amplitude contrast ratio) could be determined by recording a defocus series and measuring the positions of the zeros of 𝐻 ′ (𝑘) [START_REF] Frank | Three-Dimensional Electron Microscopy of Macromolecular Assemblies[END_REF]. For negatively staining this averaged value could be 0.35 [START_REF] Erickson | Measurement and Compensation of Defocusing and Aberrations by Fourier Processing of Electron Micrographs[END_REF][START_REF] Erickson | The Fourier Transform of an Electron Micrograph: Effects of Defocussing and Aberrations, and Implications for the Use of Underfocus Contrast Enhancement[END_REF]. For cryo-EM, the value is usually around 0.1 (0.09 [START_REF] Toyoshima | Contrast Transfer for Frozen-Hydrated Specimens: Determination from Pairs of Defocused Images[END_REF] and 0.14 (Smith and Langmore 1992)), where no heavy atom but only biological specimen is present. By changing defocus values the CTF also changes (Figure 2.07 column middle red curve), and the obtained image of the object (micrograph e.g. Figure 2.07 column right) appears differently, since the positions of zero and the range of negative phase change with the defocus value.

Estimation of the Contrast Transfer Function

The CTF may either be determined "by hand", specifically by measuring the positions of the zeros and fitting them to a chart of the CTF characteristics, or by using automated computer-fitting methods [START_REF] Frank | Three-Dimensional Electron Microscopy of Macromolecular Assemblies[END_REF].

The automated estimation of the CTF is usually realized in two steps, estimating the density of the power spectrum and then estimating the parameters of CTF. The power spectrum determines the intensity present at each spectral frequency. It can be modeled by the This model of the power spectrum must also incorporate a model of the background noise.

Two types of methods can perform the estimation of experimental power spectrum:

the conventional method such as the method of averaged periodogram [START_REF] Zhu | Three-Dimensional Reconstruction with Contrast Transfer Function Correction from Energy-Filtered Cryoelectron Micrographs: Procedure and Application to the 70SEscherichia coliRibosome[END_REF]Ferná ndez, Sanjurjo, and Carazo 1997) or the parametric methods as AR and ARMA (Velá zquez- [START_REF] Velá Zquez-Muriel | A Method for Estimating the CTF in Electron Microscopy Based on ARMA Models and Parameter Adjustment[END_REF]. The averaged periodogram method is quick and easy to estimate the spectrum but the estimations are quite noisy [START_REF] Broersen | Facts and Fiction in Spectral Analysis[END_REF]. Conversely, the parametric methods are complex and time consuming to implement, but their estimation of the power spectrum seems more accurate [START_REF] Broersen | Facts and Fiction in Spectral Analysis[END_REF][START_REF] Velá Zquez-Muriel | A Method for Estimating the CTF in Electron Microscopy Based on ARMA Models and Parameter Adjustment[END_REF].. Once the experimental power spectrum was calculated, the CTF parameters corresponding to this spectrum could be estimated by minimizing the dissimilarity between experimental and theoretical power spectrum calculated from the exhaustively predicted CTF parameters [START_REF] Zhou | CTF Determination of Images of Ice-Embedded Single Particles Using a Graphics Interface[END_REF][START_REF] Zhu | Three-Dimensional Reconstruction with Contrast Transfer Function Correction from Energy-Filtered Cryoelectron Micrographs: Procedure and Application to the 70SEscherichia coliRibosome[END_REF][START_REF] Saad | Fourier Amplitude Decay of Electron Cryomicroscopic Images of Single Particles and Effects on Structure Determination[END_REF]Huang et al. 2003;Mindell and Grigorieff 2003;[START_REF] Sander | Automatic CTF Correction for Single Particles Based upon Multivariate Statistical Analysis of Individual Power Spectra[END_REF][START_REF] Velá Zquez-Muriel | A Method for Estimating the CTF in Electron Microscopy Based on ARMA Models and Parameter Adjustment[END_REF][START_REF] Mallick | ACE: Automated CTF Estimation[END_REF][START_REF] Sorzano | Fast, Robust, and Accurate Determination of Transmission Electron Microscopy Contrast Transfer Function[END_REF]) (Figure 2.09).

CTFFind3 (Mindell and Grigorieff 2003) is one of the most utilized programs to estimate the CTF in the field of 3D electron microscopy. It estimates several parameters such as the defocus value in two principal directions, astigmatism and inclination but it does not calculate the envelope of the CTF.

Correction of the Contrast Transfer Function

Phase Flipping

The simplest correction of the CTF is "phase flipping", by performing the following operation on the image transform:

F '(k) = -F(k) for H(k) < 0 F(k) for H(k) > 0 ì í î
Which ensures that the modified image transform F(k) has the correct phases throughout the resolution domain. However, such a correction leaves the spatial-frequency dependent attenuation of the amplitudes uncorrected. Fourier components residing in regions near the zeros of H(k) are weighted down by the CTF, and those located in the zeros are not transferred at all. [START_REF] Frank | Three-Dimensional Electron Microscopy of Macromolecular Assemblies[END_REF] Phase-flipping corrections simply consist of multiplying the Fourier transform of each particle image by -1 over appropriate frequency ranges. Even so, this leaves the issue of having CTF(s) ~0 at specific spatial frequencies. Fortunately, this issue can be handled by incorporating images over a range of different defocuses (Figure 2.10) which will compensate for missing information in any one image [START_REF] Cong | Single Particle Analysis at High Resolution[END_REF].

Correcting only the sign of the CTF and disregarding the CTF-induced amplitude changes could make it possible to apply efficient alignment procedures and avoid too much low frequencies in the average (Penczek 2010a), but the amplitude correction is also necessary to achieve better signal-to-noise ratio.

Wiener Filtering

The Wiener filtering approach is shown in figure 2.11. With two degraded images having different amounts of under-focus. Graphs on the left and right show how the Wiener filter amplifies Fourier components close to the zeros, but does not go beyond a certain amplitude, whose size depends on the signal-to-noise ratio -SNR. The graph below the restored image shows the effective transfer function achieved after combining the information from both images. It is seen that two zeros have been entirely eliminated, while a third one, at 1/11Å -1 , is not, since it is shared by both CTFs. The Wiener filtering corrects the amplitude close to zero, but does not correct the amplitude attenuation. and Scheres 2015). A series of image (a movie) could be record each time at one position of the specimen, then the total electron dose used is fractionated into image frames

Dose fragmented movie

By observing the Figure 2.12b A, we can see that the motion of particle is present in non aligned frames average. The main cause of this motion is beam damage occurring to the specimen as it is exposed to the high-energy electron beam (Glaeser, 2008, Glaeser and Taylor, 1978and Glaeser et al., 2011) although charging may also play a role (Glaeser and Downing, 2004 andHenderson, 1992). Then the carbon film is deformed, and particle could move to different direction [START_REF] Brilot | Beam-Induced Motion of Vitrified Specimen on Holey Carbon Film[END_REF].

i. Beams induced motion correction

By using a newly developed single electron-counting detector, it is confirmed that electron beam-induced motion substantially degrades resolution [START_REF] Li | Electron Counting and Beam-Induced Motion Correction Enable near-Atomic-Resolution Single-Particle Cryo-EM[END_REF]. This beaminduced motion should be corrected at the individual particle level during refinement of the 3D reconstruction [START_REF] Campbell | Movies of Ice-Embedded Particles Enhance Resolution in Electron Cryo-Microscopy[END_REF]. Yifan Cheng's results indicated that the motion speed is very high in the first few sub frames and that removing these sub frames resulted a better 3D reconstruction [START_REF] Li | Electron Counting and Beam-Induced Motion Correction Enable near-Atomic-Resolution Single-Particle Cryo-EM[END_REF]. The figure 2.12 shows that by correcting the frames The translations for the individual frames were calculated from the translations determined in Fig. 3 for the 10-frame averages by linear interpolation. The frame average in panel B exhibits substantially improved contrast with details at higher resolution that were not visible in panel A.

motion, the resolution of the 3D reconstruction can be improved (from red curve to purple curve). Removing the first few sub frames from the 3D reconstruction makes the resolution a little higher.

i. Low Dose and radiation damage

The radiation damage, which affects the structure and/or the chemistry of the specimen, depends on the incident-beam energy, and is considered undesirable.

An electron beam is a stream of high-energy particles that are bombarding the sample.

The image that is viewed is a result of the interaction of the sample with this beam. Most of the electrons that form the high-resolution image appear due to elastic scattering, where only their trajectory has been changed, but their energy is unaffected. However, a small fraction of the electrons transfers some of their energy to the sample.

This energy accumulates and can break apart molecular bonds, destroying the sample after some time. Therefore, for high-resolution imaging, low dose parameters require that the area to be imaged is not exposed until the picture is actually taken. All image calibration and focusing is done beforehand on a nearby area, in the hope that its properties are similar to the final imaged area. For the final imaging, very low electron doses on the order of 15-20 electrons per Å 2 are used. As a comparison, high-resolution electron microscopy of semiconductors routinely uses doses of 100 000 electrons per Å 2 .

It has been shown that the bubbling appeared from 50e -/Å 2 [START_REF] Lepault | Electron Microscopy of Frozen Biological Suspensions[END_REF]) and structural fine details were lost in 25-40e -/Å 2 compared to 15-20e -/Å 2 [START_REF] Conway | The Effects of Radiation Damage on the Structure of Frozen Hydrated HSV-1 Capsids[END_REF]).

The dose fragmented movie makes it possible to optimize images affected by radiation damage. Since later frames in the movie represent a high accumulated dose, one way is to remove a subset later frames from the movie (Figure 2.12 blue).

Super Resolution Mode

The K2 SummitTM Super-Resolution mode takes counting further and surpasses the theoretical information limit defined by the physical pixel size. The K2 TM sensor was carefully designed such that the point spread function -PSF is slightly larger than the 5um physical pixel size. As a result each incoming electron deposits signal in a small cluster of pixels. The high-speed K2 SummitTM electronics is able to recognize each electron event at 400 frames per second and find the center of that event with sub-pixel precision.

Single Particle Analysis

Single particle analysis is a cryo-EM procedure where individual macromolecular complexes that are frozen in a thin layer of vitreous ice are imaged. The 3D structure of the complex is reconstructed from projection images of individual complexes (called particles) in different relative orientations. (Bai, McMullan, and Scheres 2015)

Selection of Particles: Manual or Automatic

Particles can be picked manually or automatically from selected micrographs initially checked without astigmatism by the power spectrum. EMAN boxer (Ludtke, Baldwin, and Chiu 1999), or SPIDER and WEB [START_REF] Shaikh | SPIDER Image Processing for Single-Particle Reconstruction of Biological Macromolecules from Electron Micrographs[END_REF]) are the most popular software used for this step. Particle images and particle coordinates are saved in separate files. Different automatic approach are also available [START_REF] Zhu | Automatic Particle Selection: Results of a Comparative Study[END_REF]) such as template matching approaches (Ludtke, Baldwin, and Chiu 1999;Roseman 2003;Huang and Penczek 2004;[START_REF] Wong | Model-Based Particle Picking for Cryo-Electron Microscopy[END_REF]) and feature-based approaches (Zhu et al. 2003;[START_REF] Hall | A Two Step Approach for Semi-Automated Particle Selection from Low Contrast Cryo-Electron Micrographs[END_REF][START_REF] Mallick | Detecting Particles in Cryo-EM Micrographs Using Learned Features[END_REF][START_REF] Volkmann | An Approach to Automated Particle Picking from Electron Micrographs Based on Reduced Representation Templates[END_REF]) and neural network approach [START_REF] Ogura | Automatic Particle Pickup Method Using a Neural Network Has High Accuracy by Applying an Initial Weight Derived from Eigenimages: A New Reference Free Method for Single-Particle Analysis[END_REF]).

Principe of 3D Reconstruction

Selected particles can be subjected to the 3D reconstruction, but since the particles are orientated randomly in the specimen, finding the precise correct orientation of each single particle is a very important issue.

Orientations of isolated particle images can be determined by comparing the experimental particles with the projections of a 3D map of the target molecule. Projections can be generated according to a set of predefined Euler angles (Figure 2.13), (φ, θ, ψ) or (α, β, γ) which specify one position (direction) in a spherical system.

One particle will be associated with one of the generated projections if the correlation them is the optimal. Then the Euler angles possessed by the projection are assigned to the particle. A 3D reconstruction can be done by a back-projection of all orientation assigned single particles. The density distribution across a projection is « smeared out » in the original Correlation between particles and projections can be evaluated by several different methods, from cross correlation to maximum likelihood [START_REF] Sigworth | Chapter Ten -An Introduction to Maximum-Likelihood Methods in Cryo-EM[END_REF]) and Bayesian approach (Scheres 2012b).

Starting Model

If an initial model is not available, one should build a starting (ab initio) model in the 3 dimensions based on the isolated particle images in the 2 dimensions.

Random conical tilt (RCT) (Radermacher et al. 1987) is the method normally used for this, where one zone in the grid is captured twice, tilt (45 degree) and non-tilt. This RCT method gives the right hand for the calculated structure of the particles. Another method is the common lines based on the angular reconstitution method (Van Heel 1987) [START_REF] Penczek | A Common-Lines Based Method for Determining Orientations for N > 3 Particle Projections Simultaneously[END_REF] which does not required tilt pair images and recommended for particles with high internal symmetry. Unlike the RCT method this common lines method does not give this structure with the right hand, it does not distinguish between two enantiomers.

Random Conical Tilt -RCT

The two image series used in this method are taken at a specimen tilt angle of 0° « untilted » and 45°50° « tilted » . For each specimen field, a pair of « untilted » and « tilted » images is recorded (Radermacher et al. 1987;[START_REF] Radermacher | Three-Dimensional Reconstruction of Single Particles from Random and Nonrandom Tilt Series[END_REF]).

After particles selection, an alignment of « untilted » particles is performed to determinate rotational and translational parameters, then these particles is also classified. For each class, « tilted » particles are aligned and used for the 3D reconstruction.

The alignment for the untilted images is important for the determination of the in plan rotation angle (azimuthal angle) and translational parameters X and Y, then a classification is done to distribute untilted images into different classes, ideally each class represents one distinguishable view of the object. As the tilt angle is known and the azimuthal angles are The major drawback of this technique is the missing cone. The inclined particles do not provide information within the cone defined by the angle of tilt and the axis of the electron beam. The reconstruction obtained is therefore incomplete. To correct this, the reconstruction is performed for each class of particles. The sum of aligned volumes could complete the missing information of reconstruction for each class. This method works well for providing an initial model, which needs to be refined after wards.

Common Lines

This method is based on the fact that two 2D projections of the same 3D object have a common line ( [START_REF] Heel | Angular Reconstitution: A Posteriori Assignment of Projection Directions for 3D Reconstruction[END_REF][START_REF] Penczek | The Ribosome at Improved Resolution: New Techniques for Merging and Orientation Refinement in 3D Cryo-Electron Microscopy of Biological Particles[END_REF][START_REF] Penczek | A Common-Lines Based Method for Determining Orientations for N > 3 Particle Projections Simultaneously[END_REF]. This common line can be found by comparing every central section of two 2D projections in Fourier space. For comparing each pair of 1D central section, the crosscorrelation is calculated to measure the similarity. When the common line of two 2D projections is found, the azimuthal angle of these two 2D projections is still unknown. And a third 2D projection is needed for determining the common lines between the pair of 2D

projection and the third one.

This method is called also angular reconstitution ( [START_REF] Heel | Angular Reconstitution: A Posteriori Assignment of Projection Directions for 3D Reconstruction[END_REF]. In practice, the common line search is often performed in real space by using sinogram [START_REF] Frank | Three-Dimensional Electron Microscopy of Macromolecular Assemblies[END_REF]). Further 2D projections can be compared successively with pairs of projections already solved [START_REF] Penczek | A Common-Lines Based Method for Determining Orientations for N > 3 Particle Projections Simultaneously[END_REF].

This technique requires a high signal to noise ratio and therefore works on average 2D classes of experimental images.

It is an approach particularly well suited toward objects with high symmetry, but has also been applied to asymmetric objects. It's not recommended for most particles without high symmetry, due to its susceptibility to finding incorrect structures, which lie close to local minima [START_REF] Cong | Single Particle Analysis at High Resolution[END_REF]. In addition, the use of this technique does not distinguish two enantiomers which makes it necessary to have additional information on the structure under study, or to perform at least a pair of images inclined at 0° and 15-30° to verify that the reconstruction is correct.

Ab Initio Model

The ab initio model method in EMAN2 (Ludtke, Baldwin, and Chiu 1999;[START_REF] Tang | EMAN2: An Extensible Image Processing Suite for Electron Microscopy[END_REF]) is based on a process similar to Monte Carlo methods. A set of completely random lowresolution 3D density maps are generated, and each 3D map is used as the initial model for a 3D refinement. Generally one of the refinements will find the global minimum.

In this method, the raw particle image is heavily down sampled to increase the contrast of the image and refinement speed [START_REF] Cong | Single Particle Analysis at High Resolution[END_REF]. Then the obtained putative 3D structure is evaluated, by comparing the 2D class-averages from reference free 2D classification with the 2D projection of the putative 3D structure.

There are two possible issues: the first risk is that if the particle has a strongly preferred orientation in the specimen, these missing regions of the objet will be filled in with particles which did not fit perfectly in other orientations, producing a deformed, but convergent structure.

The second risk is the heterogeneity or flexibility of the structure. If the specimen is sufficiently flexible in solution, then the algorithm can confuse changes in conformation with changes in orientation, producing distorted self-consistent structures. In such cases, both the Monte Carlo method and the common-lines based approach are very likely to produce incorrect structures (Cong and Ludtke 2010).

2D Classification

The first step for structure determination in single particle analysis is the 2D analysis.

The selected 2D images of the particles are aligned and separated into homogenous groups of particles. In this way, bad particles and image artifacts should be removed by removing groups of particles with a featureless or meaningless 2D average image.

Various strategies proposed for the alignment and clustering of particle images are all based on the K-means classification algorithm. This algorithm or its variant is used in 2D reference free alignment, 2D multi-reference alignment (MRA), and also in 3D refinement.

For example, in 2D multi-reference alignment, K seed images are selected randomly from the dataset of images at the beginning. Then by comparing particle images with the K seed images, every particle image is assigned to one seed image, where the similarity between them is optimum. Then K new seed images is calculated by averaging the K groups of translational and rotational aligned particle images. And we can compare particle images with these K new seed images, and repeat iteratively this classification procedure until the convergence is reached -very few particle image changes its assigned seed image during one iteration. Then the K 2D average images of these K groups are investigated, only good particles in the classes with a high quality class average will be selected for the 3D analysis.

Beyond the classic 2D classification approaches, ML (maximum likelihood)

approaches have recently emerged: a multi-reference alignment scheme called ML2D [START_REF] Scheres | Maximum-Likelihood Multi-Reference Refinement for Electron Microscopy Images[END_REF] and a neural network kerdenSOM (Pascual-Montano et al., 2001). The ML2D algorithm could be used for simultaneously alignment and classification of singleparticle images [START_REF] Scheres | Maximum-Likelihood Multi-Reference Refinement for Electron Microscopy Images[END_REF]. Reference-free class averages are computed in a completely unsupervised way by starting multi-reference alignments from average images of random subsets of the unaligned particles. The only parameter that is adjusted is the number of references (classes) K. The more classes, the fewer particles participate in each class (the lower the weighted sums of particle contributions to each class). Averaging over a low number of particles leads to noisy class averages, which result in suboptimal alignments and classifications. Therefore, in practice, K is often limited so that there are at least 200-300 cryo-EM particles per class, and these numbers of particle per class could be much lower for negative stain data (Scheres 2010).

An intrinsic characteristic of the ML approach is that it does not assign images to one particular class or orientation. Instead, images are compared with all references in all possible orientations and probability weights are calculated for each possibility. Class averages are then calculated as weighted averages over all possible assignments and used for the next iteration [START_REF] Scheres | Maximum-Likelihood Multi-Reference Refinement for Electron Microscopy Images[END_REF]. The CTF-affected signal and non-white noise are used in the MLF2D algorithm [START_REF] Scheres | Disentangling Conformational States of Macromolecules in 3D-EM through Likelihood Optimization[END_REF]. An additional advantage is multiresolution approach where higher frequencies are only included in the later optimization process if the class averages extend to such resolutions (Scheres 2010).

3D Classification

3D reconstructions from cryo-EM single-particle projections typically provide much more information than 2D class averages. And 3D reconstructions often depend on the availability of an suitable initial model, which may be derived from known structures of similar complexes or be obtained by angular reconstitution or random conical experiments (Scheres 2010).

It's often difficult to obtain a suitable and reliable initial model even for structurally homogeneous data sets, and bias toward incorrect models may introduce important artifacts in the results. And it is more complex if heterogeneity is present in the image dataset. The combination of distinct conformations in a single reconstruction leads to a general loss of resolution or to the loss of electron density in specific areas for partially flexible complexes (Scheres 2010).

But, structural heterogeneity give us also an opportunity to obtain information of possible different functional structures.

3D multi-reference classification by ML (ML3D) [START_REF] Scheres | Disentangling Conformational States of Macromolecules in 3D-EM through Likelihood Optimization[END_REF] depends on an initial 3D reference and the selection of a suitable model has been found to be a pivotal step for successful classification. In many cases, a suitable starting model may be obtained by refinement of the whole dataset against a 3D reference, and low-pass filtered (Scheres 2010).

The direct use of low-pass filtered models from the Protein Data Bank (PDB), negative stain reconstructions as starting models in ML3D classification has been observed to yield suboptimal results (Scheres 2010). It is often better to first refine such a model against the complete structurally heterogeneous group of particles, to remove false low-resolution features from the model (Scheres 2010). For example, negative stain models may be flattened, and atomic models may be incomplete or have an unrealistically high contrast (Scheres 2010).

Multi-reference ML refinements were observed to converge to useful solutions when starting from initial models (seeds) that are random variations of a single consensus model.

To generate randomly different models (seeds), the structurally heterogeneous dataset could be divided into random subsets and performs a single iteration of ML3D refinement of the consensus model against each of the subsets separately (Scheres 2010).

The most important parameter in 3D classification is K, the number of 3D models (references) refined simultaneously. The number of references may be determined by the amount of experimental particle images available and the capacity of the calculation source.

Often, asymmetric reconstructions from less than 5000 to 10,000 cryo-EM particle images could be too noisy for reliable classification.

Interpretation of the classification results:

The problem of model bias plays an important role in many 3D refinement programs for cryo-EM single-particle data. When the references do not gain new structural details, it could be that the refinement process is affected by model bias. Often, the refined structures remain similar to the initial 3D models at intermediate-low resolution and only seem to accumulate noise at higher frequencies.

A good indication that the refinement is not affected by model bias is that various, different starting models all converge to a similar solution (Scheres 2010).

Reproducibility: It is often useful to check the reproducibility of the 3D classification results by comparing 3D classes from multiple 3D classification runs that were started from different random models. Significant class overlaps (more than 75%) are usually an indication (Scheres 2010).

3D Refinement

3D Refinement is the most time consuming and critical stage in single particle analysis.

While there are a number of different strategies for refinement, such as cross common-lines ( [START_REF] Heel | Angular Reconstitution: A Posteriori Assignment of Projection Directions for 3D Reconstruction[END_REF], maximum likelihood methods [START_REF] Sigworth | A Maximum-Likelihood Approach to Single-Particle Image Refinement[END_REF][START_REF] Scheres | Maximum-Likelihood Multi-Reference Refinement for Electron Microscopy Images[END_REF][START_REF] Scheres | Disentangling Conformational States of Macromolecules in 3D-EM through Likelihood Optimization[END_REF][START_REF] Scheres | Maximum Likelihood Refinement of Electron Microscopy Data with Normalization Errors[END_REF][START_REF] Sigworth | Chapter Ten -An Introduction to Maximum-Likelihood Methods in Cryo-EM[END_REF], maximum likelihood with priors referred as the Bayesian or maximum a posteriori approach (Scheres 2012b; Scheres 2012a), and global minimization (Yang, Ng, and Penczek 2005).

The projection matching 3D refinement is iterative, with 4 steps in each iteration: 1)

Projections of the current 3D model are generated over all possible orientations within an asymmetric unit; 2) 2D classification, each of the 2D particle images is computationally compared with the 2D projections of the 3D model and assigned to the most similar projection; 3) Class-averaging. The 2D particle images assigned to each projection are iteratively aligned and averaged to compute class averages; and 4) 3D reconstruction, the class averages with assigned projection orientations are combined to produce a new 3D reconstruction. These four steps are then iterated until some convergence criterion has been met [START_REF] Cong | Single Particle Analysis at High Resolution[END_REF].

Bayesian approach

The RELION (Scheres 2012a) program has been used fundamentally in recent researches for macromolecular structures by single-particle analysis of cryo-EM in structural biology. It uses a Bayesian approach to infer parameters of a statistical, and a gold standard Fourier shell correlation (FSC) procedure to prevent over fitting [START_REF] Scheres | Prevention of Overfitting in Cryo-EM Structure Determination[END_REF].

In the Bayesian approach to cryo-EM structure determination, the reconstruction problem is expressed as the optimization of a single target function (Scheres 2012b). The reconstruction problem is formulated as finding the model that has the highest probability of being the correct one considering both the observed data and available prior information.

Optimization of this posterior distribution is called maximum a posteriori (MAP), or regularized likelihood optimization (Scheres 2012a).

Fourier Shell Correlation

The Fourier Ring Correlation (FRC) [START_REF] Saxton | The Correlation Averaging of a Regularly Arranged Bacterial Cell Envelope Protein[END_REF] was introduced to provide a measure that would be insensitive to linear transformations of the objects' densities.

For historical reasons, in 2D applications the measure is referred to as FRC while in 3D applications as Fourier Shell Correlation (FSC). FSC is a 1D function of the modulus of spatial frequency whose values are correlation coefficient computed between the FTs of two images/volumes over rings/shells of approximately equal spatial frequency (Penczek 2010b).

An FSC curve that is close to one reflects a strong similarity between two images/volumes and an FCS curve with values close to zero indicates the lack of similarity.

FSC is used for the evaluation of resolution in structure determination. The resolution is assessed by FSC curve of reconstructions from halves of the data set. Data set is first split into halves, each subset is aligned independently, and the two resulting reconstructions are compared using FSC. Typically, FSC decreases with spatial frequency and various cut-off thresholds have been proposed for serving as indicators of the resolution limit.

Disadvantages: 1) it's impossible to have independence between the two halves since the initial reference is often shared or obtained using similar principles. 2) When the number of particles is small, it's difficult to have results of alignment of half of the particles (halve)

with the comparable quality to that obtained by aligning the entire dataset. 3) Two independent halve alignments could diverge, and the reported resolution will be low.

Chapter III Fanconi: FANCD2/FANCI complex

Introduction

One of the most critical biological processes is protection and maintenance of the A manuscript related to this work has been submitted recently and is added to this chapter. However a short description of the results and conclusion is described below.

Materials and methods

Expression and purification of the human FANCD2/FANCI complex and the human FANCD2-ΔTower/FANCI complex

This step was performed in the laboratory of Dr Martin Cohn at the Biochemistry Department, University of Oxford.

FANCD2 is expressed using the pFastBac1 vector (Life Technologies) with an engineered N-terminal Flag-HA tag, and FANCL is expressed using the pFastBac1 vector (Life Technologies) with an engineered N-terminal Flag-MBP tag. For FANCD2/FANCI complex, Sf9 cell pellets were resuspended in lysis buffer (20 mM Tris-HCl pH 8.0, 0.1 M KCl, 10% glycerol and 0.2 mM PMSF), and sonicated. Lysates were clarified by centrifugation, and the supernatants were incubated with M2 anti-FLAG agarose resin (A2220, Sigma) for 2 hr. The resin was washed carefully, and the protein was eluted in the same buffer containing 0.5 mg/ml FLAG peptide.

Methods used for image analysis

Negatively stained freshly prepared full length FANCD2/FANCI was applied to glowdischarged, carbon-coated grids and allowed to adsorb for 15-60 s. Specimen was then stained with 2% uranyl acetate. Vitrified full length FANCD2/FANCI and C-terminus deleted FANCD2/FANCI was prepared on glow discharged carbon quantifoil grids. Specimens were imaged at a nominal magnification of 30,000× with a 2k×2k Gatan CCD camera (corresponding to a pixel size of 3.5 Å at the specimen level) in a JEOL 2100, LaB6 operating at 200kV. A preliminary full length FANCD2/FANCI 3D model was calculated using the random conical tilt method (RCT) (Radermacher et al., 1987) and the WEB and SPIDER software package (Frank et al., 1996). For this first 3D model, 4082 pairs of particles of full length FANCD2/FANCI were picked from 80 tilt pair images recorded at 50° and 0°.

Refinement of the 3D volume obtained from the RCT method was done using images from frozen hydrated full length FANCD2/FANCI. 5058 particles were selected after using Roseman's algorithm (Roseman, 2003) in SPIDER (Frank et al., 1996) procedure and subsequently manual selection in WEB (Frank et al., 1996). Defocus was determined using CTFTILT (Mindell and Grigorieff, 2003). 3D classification and refinement were carried out

Structure of the truncated human FANCD2-ΔTower/FANCI complex heterodimer

Given the striking position of the Tower domain, we decided to determine whether this domain is functionally important to the FANCD2/FANCI complex. The position of the Tower domain suggested that it is composed of the C-terminus of FANCD2. To test this directly, we introduced a C-terminal deletion in FANCD2 containing the last 305 aa (Figure 3.3A), and purified the complex of this protein (FANCD2-ΔTower) and FANCI (Figure 3.3B).

The highly purified FANCD2-ΔTower/FANCI heterodimer was subjected to cryo-EM, resulting in a structure clearly missing the Tower domain, but otherwise similar to the fulllength structure (Figure 3.3C-D).

Conclusion

Here we provide the first structural insight into the human FANCD2/FANCI complex by obtaining the cryo-EM structure of the complex of full-length proteins. The complex appears with an inner cavity, large enough to accommodate a double stranded DNA helix. We also discovered a protruding Tower domain, which our collaborators in Oxford have shown to be critical for the recruitment of the complex to ICLs in vitro and in vivo, and for the monoubiquitination of FANCD2. Disease-causing mutations in this domain is observed in several FA patients. We demonstrate that the complex binds strongly to a DNA structure resembling a replication fork stalled at an ICL. Finally, our work reveals that recruitment and binding of the complex to a stalled replication fork serves as the trigger for the activating monoubiquitination event. Taken together, our results uncover the mechanism of how the FANCD2/FANCI complex activates the FA pathway, and explains the underlying molecular defect in FA patients with mutations in the Tower domain.

Introduction

Fanconi anemia (FA) is a genetic disorder characterized by developmental defects, cancer susceptibility, and a severe cellular sensitivity to agents inducing toxic interstrand crosslinks (ICLs) formed between the two strands of the Watson-Crick DNA helix. 19 FA proteins function together in a complex pathway leading to the repair of an ICL, primarily in the S-phase of the cell cycle (Jo and Kim 2015). Central to the pathway is the FANCD2/FANCI protein complex, recruitment of which to the ICL is critical for repair. The underlying mechanism for this recruitment is currently unclear. It is known that monoubiquitination of FANCD2 is indispensable to a functional pathway (Garcia-Higuera et al. 2001), however the function of this modification is unclear. To date there is no structural information on the human FANCD2 and FANCI proteins. Here we present a cryo-EM structure of the full-length human FANCD2/FANCI complex, demonstrating the existence of a novel Tower domain. The Tower domain defines recruitment of the complex to ICLs in vitro and in vivo. We also demonstrate, for the first time, a direct interaction between the FANCD2/FANCI complex and a DNA structure resembling a replication fork stalled at an ICL. Finally, we present data establishing that the FANCD2/FANCI complex needs to be bound to DNA, in order to be monoubiquitinated.

Results

Cryo-EM structure of the full-length human FANCD2/FANCI complex.

To gain further insight into the mechanism of FANCD2/FANCI function during ICL repair, we decided to obtain structural insight of the full-length human complex by electron microscopy (EM). We co-expressed Flag-FANCD2 and His-FANCI in Sf9 cells and purified the heterodimeric complex to homogeneity (Figure 1A). The intensities of FANCD2 and FANCI in the Coomassie stained gel are nearly identical and analytical ultracentrifugation confirmed a stoichiometric 1:1 heterodimer (data not shown). The highly pure complex was then subjected to EM analysis using negative stain. Representative micrographs of the grids demonstrated homogeneity of the sample (Figure S1A). 4082 pairs of particles were used to calculate a 3D model using the random conical tilt method (Radermacher et al., 1987).

We then subjected the FANCD2/FANCI complex to cryo-EM in order to refine this preliminary 3D model. A micrograph shows homogeneity of the sample (Figure S2A), from which we obtained a structure with a resolution of 22 Å, allowing the determination of further details of the structure. The 3D reconstruction revealed a 90 Å wide and 160 Å long structure with a hollow central part. We also observed a characteristic 60 Å protruding domain, which we termed the Tower domain. (Figure 2A). We noticed that the end of the Tower domain adapts a fork-like structure. When comparing our determined structure to the crystal structure of the mouse FANCD2/FANCI complex (Joo et al. 2011), we noticed a striking difference.

While the position of the Tower domain is vertical in our structure, is adapts a horizontal position in the crystal structure (Figure 2B). When we dock the crystal structure onto the cryo-EM structure, allowing flexibility of each of the 8 solenoids in the crystal structure relative to each other we observe good agreement between the two structures (Figure 2C). The difference in the position of the Tower domain in the two structures could be a result of the two different methods used to obtain the structures. The crystal structure is based on a version of FANCD2 containing 3 large deletions, whereas the EM structure is based on the full-length protein, which might also contribute to the observed differences. The position of the Tower domain suggested that it is composed of the C-terminus of FANCD2. To test this directly, we introduced a C-terminal deletion in FANCD2 containing the last 305 aa (Figure 3A), and purified the complex of this protein (FANCD2-ΔTower) and FANCI (Figure 3B). The highly purified FANCD2-ΔTower/FANCI heterodimer was subjected to cryo-EM, resulting in a structure clearly missing the Tower domain, but otherwise similar to the full-length structure (Figure 3C-D).

The Tower domain is functionally important for the FANCD2/FANCI complex

Given the striking position of the Tower domain, we decided to determine whether this domain is functionally important to the FANCD2/FANCI complex. We reduced the cellular levels of endogenous FANCD2 to less than 5% in HeLa cells by shRNA, and then stably expressed exogenous EGFP-tagged either full-length FANCD2 or FANCD2-ΔTower (Figure S3A). Since the FANCD2-ΔTower protein was not as strictly localized in nucleus as the fulllength protein, we also established a cell line expressing the EGFP-FANCD2-ΔTower-NLS, which includes an engineered nuclear localization sequence. The ability of these cell lines to survive under increasing concentrations of MMC was then assessed. We found that noncomplemented cells were very sensitive to MMC compared to control cells and that expression of the full-length protein partially restored resistance (Figure 4A). On the other hand, expression of the proteins containing the Tower deletion, did not complement the cells, demonstrating the functional importance of the Tower domain (Figure 4A). Monoubiquitination of FANCD2 is necessary for its ICL DNA repair function. We speculated that the Tower domain might affect the ability of FANCD2 to be monoubiquitinated, and that this could be part of the mechanism underlying its function in the pathway. We expressed full-length FANCD2 and FANCD2-ΔTower in FANCD2 deficient PD20 cells, and assessed the ability of the proteins to be monoubiquitinated. As expected, full-length FANCD2 was strongly ubiquitinated in response to MMC (Figure 4B, lane 10). In contrast, the FANCD2-ΔTower protein was not ubiquitinated at all (Figure 4B, lane 12). Also, we observed a nice restoration of FANCI ubiquitination when full-length FANCD2 was expressed, but no ubiquitination of FANCI when the FANCD2-ΔTower protein was expressed. The absence of ubiquitination of FANCD2 could be due to lack of interaction with its E3 ubiquitin ligase complex (core complex). However, both full-length and the Towerdeletion protein interact equally well with the core complex (Figure S3B). Since the Tower domain is important for the function of FANCD2, and also for its monoubiquitination, we next assessed whether the Tower domain is important for the recruitment of FANCD2 to ICLs in vivo. To this end we utilized a live-cell imaging system that we have previously described (Liang et al., 2015). Full-length FANCD2 was recruited to ICLs as expected, forming clear stripes 10min after the introduction of the ICLs (Figure 4C). Strikingly, deletion of the Tower domain completely abolished the recruitment (Figure 4C and Figure S4A).

The Tower domain of FANCD2 interacts directly with ICLs and determines the recruitment of the FANCD2/FANCI complex to chromatin

Since the Tower domain is functionally important, due to its requirement for monoubiquitination of FANCD2, and its role in the recruitment of FANCD2 to ICLs in vivo, we speculated that this domain might be involved in the interaction of the FANCD2/FANCI complex with DNA. To test this directly, we assessed the ability of the FANCD2/FANCI and FANCD2-ΔTower /FANCI complexes to interact with DNA. It is presumed that the FANCD2/FANCI complex is recruited to replication forks stalled at an ICL. Therefore, we synthesized a DNA molecule mimicking a replication fork and evaluated the abilities of the two complexes to interact with it. FANCD2/FANCI formed a specific complex with the DNA, which could be super-shifted using specific antibodies (Figure 5A, lanes 2-3). On the other hand, FANCD2-ΔTower/FANCI failed to form any complex with the DNA, demonstrating the importance of the Tower domain in the protein/DNA interaction (Figure 5A, lanes 4-5).

To gain further insight into the mechanism of the interaction, we next synthesized a similar DNA molecule, but now containing an ICL in the fork, more realistically representing a replication fork stalled at an ICL. Using this molecule, we again observed abrogation of DNA binding when the Tower domain was deleted (Figure 5B). Interestingly, we found that FANCD2/FANCI interacted significantly stronger with the fork containing an ICL, than with the non-crosslinked counterpart. Titrating the amount of protein in the binding reaction confirmed the observed preference (Figure 5C).

FANCD2/FANCI is first recruited to DNA, then ubiquitinated by FANCL

Our data demonstrate a direct interaction between the FANCD2/FANCI complex and a replication fork DNA structure containing an ICL, and that the interaction is dependent on the Tower domain in FANCD2. We also know that the Tower domain is required for monoubiquitination of FANCD2. Therefore, we hypothesized that perhaps the reason the FANCD2-ΔTower is not ubiquitinated is that the protein is not recruited to DNA, and therefore not ubiquitinated. If that were the case, it could mean that FANCD2/FANCI has to be bound to DNA in order to be ubiquitinated. To test this hypothesis, we reconstituted the monoubiquitination reaction in vitro, using recombinant proteins (Figure S4B). We then first assessed the monoubiquitination of FANCD2 in the absence of DNA, and observed only very weak monoubiquitination (Figure 5D, lanes 2-4). However, when we added DNA to the reaction, a robust monoubiquitination was observed (Figure 5D, lanes 5-7), consistent with a previous report (Sato et al., 2012). The increase in specific monoubiquitination was not due to increased general E3 ligase activity in the reaction (Figure S4C). Interestingly, we observed only modest monoubiquitination of FANCI, in good agreement with previous reports (Figure S4D) (Longerich et al. 2014;Sato et al. 2012). On the other hand, no monoubiquitination was observed when we subjected the FANCD2-ΔTower/FANCI complex to the same assay (Figure 5D,. Taken together, this suggests that the FANCD2/FANCI complex must be bound to DNA in order to be activated by monoubiquitination, and that the Tower domain is required for this mechanism. Of particular interest, we found that four Fanconi Anemia patients have disease-causing mutations in the Tower domain (Kalb et al. 2007), underscoring the functional importance of this domain (Figure 5E).

Discussion

Activation of FANCD2 by monoubiquitination is an initial and critical event in the Fanconi Anemia DNA repair pathway. The mechanism underlying this process has remained elusive. Here we present the cryo-EM structure of the full-length human FANCD2/FANCI complex, and uncover the existence of a novel Tower domain of FANCD2. We demonstrate that the Tower domain is required for a direct interaction with a DNA structure mimicking a replication fork arrested at an ICL, and that this interaction triggers the monoubiquitination event. Importantly, our work determines that the FANCD2/FANCI complex is recruited to DNA before it is monoubiquitinated, rather than what was previously through, namely that the monoubiquitination precedes, and results in, recruitment of the complex to DNA.

The cryo-EM structure of the human FANCD2/FANCI complex is largely in agreement with the previously reported crystal structure of the mouse homologs (Joo et al., 2011). The only significant difference is the position of the Tower domain, which appears vertical in our structure, and horizontal in the crystal structure. There are several possible explanations for the difference. First, it might be that the mouse and human proteins fold differently. Second, three deletions were introduced in the mouse FANCD2 protein used for crystal formation, including a large deletion at the base of the Tower. It is possible that this region serves as a hinge, which upon deletion causes the Tower to adapt a new confirmation.

Third, the FANCD2/FANCI complex used in our structure determination was formed in vivo and purified as a complex, whereas the mouse proteins were purified as monomers and the complex assembled in vitro. Fourth, the techniques used, hence the state of the protein samples used to obtain the two structures are principally different, which might affect the conformation that the proteins adapt while being analyzed.

It is known that the FANCD2/FANCI complex possesses some DNA binding activity (Joo et al. 2011;Park et al. 2005), however the true substrate during ICL repair has remained elusive. Using purified components we have discovered that the FANCD2/FANCI complex preferentially binds to a DNA structure mimicking a replication fork stalled at an ICL. This finding is in good agreement with existing literature showing a function of the complex during replication-dependent repair of ICLs (Knipscheer et al. 2009). We corroborated these results by demonstrating a timely recruitment of FANCD2 to ICLs in vivo.

Monoubiquitination of FANCD2 is essential to its function in ICL repair. A point mutation of the modified lysine 561 to arginine, leads to a complete loss of function of the protein. However, the functional consequence of this critical monoubiquitination is unknown.

One possibility is that the ubiquitination affects the ability of FANCD2 to be recognized by a protein facilitating the recruitment of FANCD2 to DNA. If that were the case, it would entail that FANCD2 is ubiquitinated before it is recruited to DNA, which is indeed the current understanding. However, we found that monoubiquitination of FANCD2 is strongly stimulated by DNA, suggesting that the protein is first recruited to DNA, likely through binding to a replication fork stalled at an ICL, and thereafter monoubiquitinated. Indeed, it has been shown that the FA core complex, which is the E3 ligase responsible for monoubiquitinating FANCD2, is recruited to chromatin via FANCM and FAAP24 (Ciccia et al. 2007;Kim et al. 2008), consistent with the notion that ubiquitination takes place on DNA (Castella et al. 2015). It is possible that FANCD2 undergoes a conformational change upon binding to the ICL, and that this allows the monoubiquitination to take place. Ongoing structural studies should clarify this. It is possible that after FANCD2/FANCI is monoubiquitinated it acquires even higher affinity for the ICL (Figure 5F). It is also plausible that the monoubiquitinated form of FANCD2/FANCI interacts with a chromatin associated factor, ensuring retention after modification.

Experimental procedures

Cell lines, antibodies, and plasmids

HeLa and PD20 cells were grown in DMEM (D5796, Sigma) supplemented with 2.5-10% FBS. Antibodies used were as follows: anti-FANCD2 (sc-20022, Santa Cruz Biotechnology); anti-FANCI (FARF); and anti-α-tubulin (5829, Millipore). EGFP-fused FANCD2 cDNA was expressed using the pOZ-N plasmid as described (Liang et al., 2015).

shRNA-mediated knockdown of the FANCD2 gene was achieved by expressing the target sequence 5'-GAGCAAAGCCACTGAGGTA-3' in the pSuper.retro vector (Clontech).

Transfections of plasmid DNA were carried out using FuGENE6 (Promega) according to the manufacturer's instructions. The FANCD2 tower domain deletion plasmid was generated by deleting amino acids 1,147-1,451.

Electrophoretic mobility shift assay (EMSA)

EMSA was performed as previously described (Zheng et al. 2011) with the following modifications: The binding reaction that contained 0.5 μg of FANCD2(ΔTower)/FANCI complex and 1 nM radiolabeled DNA, was performed in 10 μl containing 14mM Tris-HCl pH 8.0, 100 mM NaCl, 3.4% glycerol and 1 mM dithiothreitol (DTT). For super-shift 0.5 μg anti-HA antibody was added.

Preparation of whole cell lysate

Cells were scraped off the dishes, and centrifuged at 1,000 rpm for 5 minutes. Cell pellets were resuspended and incubated in equal volume of Benzonase buffer (2 mM MgCl2, 20 mM Tris pH 8.0, 10% glycerol, 1% Triton X-100 and 12.5 units/ml Benzonase (E1014, Sigma) on ice for 10 minutes. The cells were then lysed by the addition of an equal volume of 2% SDS to reach a final concentration of 1%. Samples were heated at 70°C for 2 minutes.

The protein concentration was determined by Bradford assay (Bio-Rad Life Science).

Live-cell imaging

EGFP-fused wildtype and mutant FANCD2 cDNA were inserted into the pOZ vector as described above. Live cell imaging were carried out with an OLYMPUS IX81 microscope connected to PerkinElmer UltraView Vox spinning disk system equipped with a Plan-Apochromat 60x/1.4 oil objective using Volocity software 6.3 for image capturing. EGFP excited with 488 nm nm laser lines, respectively. Throughout the experiment, these cells were maintained at 5% CO2 Confocal image series were typically recorded with a frame size of 512x512 pixels and a pixel size of 139 nm. For localized DNA damage induction, cells were seeded in glass bottom dish (MatTek) and sensitized by incubation in DMEM supplemented with 10% FBS and 10 μg/ml 4,5′,8-trimethylpsoralen (TMP) for 30 min at 37C. Microirradiation was performed using the FRAP preview mode of the Volocity software by scanning (each irradiation time was 100 ms) a preselected 3-5 stripes (50x3 pixels) within the nucleus 10 times with a 405nm laser set to 100% laser power. The EGFP intensities at microirradiated sites were quantified using ImageJ with Fiji, and normalized by their intensities before microirradiation.

Cell fractionation

Harvested cell pellets were permeabilized with CSK buffer containing 200mM NaCl, 10mM PIPES, 300mM Sucrose, 1mM MgCl2, 1mM EDTA and 0.5% Triton X-100 on ice for 10 minutes. CSK fraction (supernatant) and nuclear pellet were separated by centrifugation at 3,000 rpm at 4 degree for 10 minutes. Nuclear pellet was processed the same way as whole cell lysate described above.

Clonogenic survival assay

Cells (250-4,000) were plated in 6-well plates and treated with different dosages of MMC on the next day. Colony formation was scored after 10-14 days using 1% (w/v) crystal violet in methanol.

Co-immunoprecipitation

PD20 and PD20 expressing FLAG-HA tagged FANCD2 and FANCD2-ΔTower cells were treated with 160ng/ml MMC overnight. Cell pellets were incubated with Buffer A (0.5% Triton X-100, 20 mM Tris pH 8.0, 2mM MgCl2, 5mM MgCl2, 10% Glycerol, 50 unit/μl Benzonase for 10 minutes on ice. 10 times pellet volume of Buffer B (0. 5% Triton X-100, 20mM Tris-HCl pH 8.0, 2mM MgCl2, 10% Glycerol, 150mM KCl and 0.2mM PMSF) was added to the mixture and incubated for 10 minutes for extraction. Lysates were clarified by centrifugation, and supernatant was used for immunoprecipitation. M2 agarose beads was added to the lysates, and incubated for 2 hours. The resin was washed extensively, and eluted with 0.5mg/ml FLAG peptide.

In vitro ubiquitination assay

The in vitro ubiquitination assay is done as described (Hodson et al. 2014). The movies from the fresh sample were recorded at a magnification of 40410x in super resolution mode on a direct electron detector camera with a total dose of 60 electrons per square Angstrom. They were binned two times (2x binned), corrected for drift and averaged to yield an average image for each movie.

Andreas Schenk collect these movies in the team of Tomas Walz at Harvard Medical School, Boston, where they possess a microscope of F20 FEI 200keV equipped with a direct electron detection camera Gatan Summit K2. The 2 times binned images have a pixel size of 1.24Å/pixel. And the image size is 3712x3712 pixels. The defocus values are from 0.8 to 3.5 μm. Each 2x image is a sum of a set of 31 frames with a very short exposure time (300 ms per frame). During the data acquisition the sample was exposed to an electron dose of 1.94 electrons per square Å for each frame. This means that the later a frame is located in the series, the more beam damage of the sample will show in the frame. The dose we chose for recording is quite high to increase the contrast. For the final reconstruction, several frames from the dose-fragmented movies should be used.

For the classification and 3D reconstruction we used all frames to get maximal contrast. This will make the picking, alignment and classification easier and more reliable.

However, the 3D reconstruction will show the beam damage. For this step, we can take advantage of having individual frames. One could for example just sum up the first 21 frames, instead of using all 31. This will reduce the beam damage in the summed image. This new image will show exactly the same view as the old one. Therefore by using these new images and recalculating the 3D map, without having to redo the full processing, the resulting 3D map should show much less beam damage.

Beams introduced motion correction

First of all, the recorded movies were aligned at the whole-image level by using MotionCorr [START_REF] Li | Electron Counting and Beam-Induced Motion Correction Enable near-Atomic-Resolution Single-Particle Cryo-EM[END_REF], which estimate for each frame of the movie a vector to describe the motion comparing the previous frame. Not only drift and astigmatism but also beamintroduced motion is corrected. We can see that in the no corrected micrograph (Figure 4.01 left), particle region is blurry and shapeless. But after the motion correction step (Figure 4.01 right), we can easily identify the location and shape of the particles, which then have a better contrast for further image processing.

Images of PhK

Particles Selection

All particles are selected manually by using EMAN boxer (Ludtke, Baldwin, and Chiu 1999) from the global motion corrected (MotionCorr) 2x binned micrographs. Original micrographs are low passed at 18 Å for a better visualization contrast. I selected initially 26516 particles from one preparation with which I made most of my image analyses then in another campaign towards the end of my thesis, I selected a further 6803 particles, so this made a total of 33319 particles. We didn't eliminate any particle or class at this step except obvious contaminations because we are expecting possible conformational changes. The third model, model 3 is an ab initio model built by using SPARX from 26516 particles selected in this study. The method used the following procedure: the CTF for the images was first determined using sxcter.py. This information together with the box files were then used to window the particles with the newest beta version of sxwindow.py. Then we were trying to find out representative and reproducible 2D class averages from the selected particles. This step (ISAC classification) has been repeated several times until no more new class average could be found. Then a common line based algorithm (Viper) was run to find an initial 3D reconstruction from these class averages. The final model is shown in figure 4.07 column C.

Comparison between significant particles and class average

We have also tried SIMPLE [START_REF] Elmlund | SIMPLE: Software for Ab Initio Reconstruction of Heterogeneous Single-Particles[END_REF] to build an ab initio model, but the result was not satisfactory mostly because this program is using individual particles rather than class averages and unfortunately our single images have a very low signal-to-noise ratio. (1)

(2) (3) (4) The results suggest that there must be an important inconsistency between the starting model 2 and the data, as no clear bridge is formed when K=1,2. Another possibility was that the four bridges region becomes less important in intensity level after the initial low pass filter applied to the starting model. However, those four bridges are never recovered from any 3D classification running.

Comparison: classification results from model 1 and 2

By comparing the significant 2D class averages from reference free 2D classification (Figure 4.12 row A) and the significant 3D classification results from two different models (Figure 4.09 and 4.11), we found that these 3D results in Chimera solid mode look similar to those 2D class averages (Figure 4.12 B( 2)C(2) to A(left), B( 4)C( 4) to A(right)).

We believe that the structure of the enzyme is probably with two bridges and four peaks in each lobe, because this structure can be obtained from two obviously different models (Figure 4.12 row B row C).

Then we think that the active PhK Model 1 (Figure 4.09 C) is closer to the underlying optimal structure, since the resulting volume (2 bridges linking 2 lobes with 4 peaks on each side) is easier to achieve with this model. The inactive PhK Model 2 (with 4 bridges, Figure 4.11 C) will therefore not be used anymore for subsequent image processing.

In the next chapter I will describe more 3D classifications with the Model 1 and the ab initio Model 3 (Figure 4.07 column C) generated by using SPARX [START_REF] Hohn | SPARX, a New Environment for Cryo-EM Image Processing[END_REF].

( At a resolution of 19Å, these two volumes of "Class3" calculated from two different starting models 1 and 3 look very similar. This is interesting and encouraging that these two resulting volumes were found to converge to almost the same structure.

) (2) (3) 1
The major significant structural details after this 3D classification using both model 1 and 3 as starting model are: two bridges linking the two lobes with four peaks on each side.

The extended bridge region and the two of the four peaks outside the model should be emphasized (Figure 4.17, black arrow) since none of the starting models contains this information.

( In the following, to test the significance of those classes "Class3", a 3D classification using K=1 for keeping only one class was performed. One thing should be mentioned which is of real interest is that, when we perform a 3D classification with only one class (K=1), we can also obtain a volume which is really close to the converged map mentioned above (Figure 4.18).

) (2) (3) 1
One explanation could be that good particles representing real underlying structure information have better Signal to Noise Ratio values, and higher weighted probability values, which make more contribution to the reconstructed volume. Contrarily, bad particles containing too much noise have worse Signal to Noise Ratio values, and the weighted probability value are lower, the reconstructed volume is less influenced by these particles.

Another explanation could be that, in very difficult case -with low Signal to Noise Ratio, when we use a K-mean like classification, the program is forced to make initially different K classes, and this may create meaningless structure (Class1 and Class2 e.g. (Figure 4.13 column 1)2); Figure 4.14 column 1)2)), which is subsequently reinforced by the high level noise in data [START_REF] Henderson | Avoiding the Pitfalls of Single Particle Cryo-Electron Microscopy: Einstein from Noise[END_REF].

So the particles of the two converged classes "Class3" from two different starting models should have a better Signal to Noise Ratio than other particles, as the volume of "Class1" and "Class2" are noisier and discontinuous (Figure 4.13 column 1)2) Figure 4.14 column 1)2)), then the volume reconstructed (Figure 4.21 Upper row) by using all 26k particles (K=1) is more influenced by these particles from the two classes "Class3", and the resulting volume is very close and similar to the volume of these two "Class3" (Figure 4.13 column 3); Figure 4.14 column 3); and Figure 4.18). After several iterations, as the reconstructed volume converged to a higher resolution, relatively higher weighted probability value these particles have, then more influence and contribution they make, and closer the resulting volume is to these two "Class3".

45° 90° 90°

And then these 26k particles were subjected to two-classes (K=2) 3D classification (Figure 4.22) by using the resulting volume and particles' most-likely orientation obtained from the preceding one-class (K=1) 3D classification (Figure 4.21). It is to note that this resolution has been obtained with only 9280 particles. Which is a small number compared to our total 33319 particles.

Conclusion

In the figure above, the previously observed extended structures (Figure 4.31 orange arrow) on each side of the bridges become clearer. And we found that between this extended

Conclusion

We have obtain a non-active PhK structure better than previous studies (from 24 Å to 10 Å FSC0.5 or 7 Å FSC0.143). It is very clear that the non-active PhK also have two bridges linking its lobes as the activated PhK. 2)). 4) The alpha subunit region of the active PhK has a sphere shape, but the alpha subunit region of the non-active PhK has a peaked shape.

Further work

Fitting the gamma subunit (crystal structure) and the modeled beta subunit [START_REF] Nadeau | Structure and Location of the Regulatory Beta Subunits in the (alpha Beta Gamma delta)(4) Phosphorylase Kinase Complex[END_REF] into this cryo-EM map of non active PhK.

For now, 21 frames of the movies were used in particles polishing step in RELION.

We can also try to use polished particles with fewer frames, to decrease the influence of the irradiation damages, then achieve a higher resolution to determinate secondary structure details of PhK.

Figure 1 . 1 .

 11 Figure 1.1. Model showing how UHRF1is recruited to the interstrand crosslink, facilitating the recruitment of the FANCD2/FANCI complex ,which again precedes the recruitment of additional DNA repair factors from(Liang et al. 2015) .

Figure 1 . 2 .

 12 Figure 1.2. Human Kinome from Manning et al., 2002 Human catalytic domains are divided into nine phylogenetic groups following the sequence identity between the catalytic domains (% Id is indicated for each class)

Figure 1 . 3 .

 13 Figure 1.3. Ribbon diagram of Kinase domain showing the bilobal architecture of this domain. N-terminal lobe, formed principally from  sheet, is connected to a larger Cterminal lobe formed mainly from  helices. The catalytic loop is between these two lobes.

Figure 1 . 4 .

 14 Figure 1.4. Glycogen and glycogen phosphorylase activity A:Glycogen (black granules, red arrow) transmission electron microscopy B:Schematic two-dimensional cross-sectional view of glycogen: A core protein of glycogenin is surrounded by branches of glucose units. The entire globular granule may contain around 30,000 glucose units.C:1,4-α-glycosidic and 1,6-glycosidic linkages in the glycogen oligomer D:The phosphorylation of glycogen phosphorylase (Gpb) on a serine residue by phosphorylase kinase (PHK) is essential for the enzyme to be active. Glycogen phosphorylase activated catalyzes the phosphorolysis of the α-1,4 glycosidic bond that unites the last glucose of a branch to the rest of the glycogen molecule. The detached glucose receives the phosphoryl group on its carbon 1 (glucose 1-phosphate), while the following glucose on the branch retrieves the secondary alcohol function on his Carbone 4. In muscle, glucose 1-phosphate is then converted into glucose 6-phosphate by the action of phosphoglucomutase. the glucose 6-phosphate is the substrate for glycolysis. In liver, glucose 6-phosphate is catalysed into glucose by glucose 6-phosphatase and the glucose is released in blood circulation to maintain blood glucose at correct level. Illustration form https://en.wikipedia.org/wiki/Glycogen

Figure 1 . 5 .

 15 Figure 1.5. Activators and inhibitors signals for the Phosphorylase Kinase and the Glycogen Phosphorylase The conversion of phosphorylase kinase (b) inactive (PHK-b) onto phosphorylase kinase (PHK-a) active is performed by cyclic AMP dependent protein kinase (PKA), calcium ions can also activate PhK in absence of phosphorylation.The PHK-a will in turn catalyze the activation of its substrate, the glycogen phosphorylaseb (denoted here phosphorylase-b) into the active form, phosphorylase-a. This reaction is energy-costing: one molecule of ATP is hydrolyzed and results in the release of ADP. The PhK-a and GPa can return to their inactive state by the hydrolysis of the phosphate group by phosphoprotein phosphatase, a group phosphate mineral is then released. The action of the phosphatase is inhibited by the PPI-1, the "Phosphoprotein phosphatase inhibitor-1 "in English. The positive or negative sign indicates the nature of the action of different effector illustrated above.

Figure 1 . 6 .

 16 Figure 1.6. The inactive (GPb) and active (GPa) states of glycogen phosphorylase. Phosphorylase is a dimer. One subunit is shown in green and the other in cyan. The Nterminal region that carries Ser14 is shown in orange in both subunits. The catalytic site is marked by PLP shown in magenta. (A and B) View along the 2-fold axis of symmetry showing how, on phosphorylation, Ser14 moves from an intramolecular site in GPb to an intermolecular site in GPa at the subunit-subunit interface and contacts two arginine residues, Arg69 and Arg43' (C and D). View normal to the 2-fold axis of symmetry showing the rotation of one subunit relative to the other on phosphorylation of Ser14 that affects the orientation of the tower helices and signals to the catalytic site.

  Figure adapted from (Vé nien-Bryan et al. 2002) The arrangement of the subunits in PhK is schematic.

Figure 1 . 7 .

 17 Figure 1.7. Schematic representations of the Signalling Pathways that Lead to the Activation of Phosphorylase Kinase (PhK) in Skeletal Muscle. Figure adapted from (Venien-Bryan et al. 2002) The arrangement of the subunits in PhK is schematic.

Figure

  Figure 1.8. Electron Micrograph and Image Processing of Phosphorylase Kinase pH8.2 activated state. (A) Electron micrograph of negatively stained phosphorylase kinase. Protein is bright against a darker background. Shown are the (B and C) butterfly orientation, (D and E) chalice orientation (the chalice is viewed on its side), (F) tetrad orientation, and (G) 5 of the 19 classes averaged used to reconstruct the 3D structure. The scale bar equals 300 Å. From Vé nien-Bryan et al., 2002.

Figure 1

 1 Figure 1.9. The 3D Structure of phosphorylase kinase negatively stained Surface representation of phosphorylase kinase in different orientations. (A) front butterfly orientation, (B) chalice orientation, a rotation of 45 o of the butterfly orientation about the horizontal axis, (C) tetrad orientation, a rotation of 90 o of the butterfly orientation about the horizontal axis (D) 90 o rotation of the butterfly orientation about the vertical axis showing the rotation angle between the two lobes, and (E) oblique view showing the positions of the subunits , , and . The bar scale is 100 Å. This 3D structure will be called model 1 (activated) for further image analysis during my thesis.

  2 shows finer details (Vé nien-Bryan et al. 2009) in agreement with the

Figure 1 .

 1 Figure 1.10. Structural differences between non activated and Ca 2+ activated PhK. (A) Reconstruction of non activated PhK (B) Reconstruction of Ca 2+ activated PhK, The resolution estimated for both reconstructions was 24Å (C) Overlays of the non activated (blue) and Ca 2+ activated (red) structures. Overlapping density is indicated in purple, and contours are highlighted in white. Both conformers of the kinase (A, B) and their corresponding overlays (C) are shown in the chalice (row 1), cross (row 2), butterfly (row 3), and cube (row4) views.

Figure 1 .

 1 Figure 1.11. The 3D structure of PhK in cryo conditions compared to negative staining condition. Surface representations of PhK calculated from 18,123 selected frozen particles in different orientations. Cryo-EM and negatively stained (see Venien-Bryan et al., 2002) 3D volumes are shown (right and left respectively). (A) Front butterfly orientation. (B) Chalice orientation, from a rotation of 45 o of the butterfly orientation about the vertical axis. (C) Tetrad orientation, from a rotation of 90 o of the butterfly orientation about the vertical axis. (D) A 90 o rotation of the butterfly orientation about the horizontal axis showing the rotation angle between the two lobes. (E) Oblique view. The resolution is 10Å as estimated by 0.5FSC cut-off. The bar scale is 10 nm.

Figure1. 12 .

 12 Figure1.12. Surface representations of PhK reconstructed from unstained frozen hydrated at 25Å resolution. Model shown in the chalice (1), cross (2), butterfly (3), and cube (4) views. This 3D structure will be called model 2 (inactivated) for further image analysis during my thesis

  Figure 1.13. Location of the subunits  and  into the 3D model

Figure 1 .

 1 Figure 1.14. The signaling pathway from the hormone adrenaline to the activation of glycogen breakdown A schematic diagram showing adrenaline targeting the seven-transmembrane-helix GPCR, which activates the heterotrimeric G-protein Gαβγ , which in turn activates adenylate cyclase to produce cAMP. The cAMP activates PKA by binding to and causing dissociation of the inhibitory regulatory subunit. PKA phosphorylates PhK on the regulatory β subunit. PhK phosphorylates glycogen phosphorylase converting inactive GPb into active GPa, which initiates the breakdown of glycogen to glucose 1-phosphate (Glucose-1-P). AKAP, A-kinase-anchoring protein; C, catalytic subunit; R, regulatory subunit.Three-dimensional structures of each of the steps of the pathway. The PDB codes are: β2adrenergic receptor, 2RH1; heterotrimeric G-protein, 1GP2; adenylate cyclase-Gα complex, 1AZS; PKA-regulatory subunit complex, 2QCS; PKA active, 1ATP; PhK catalytic subunit, 2PHK; PhK holoenzyme (EM-PDB1527); GPb, 1A81; GPa, 1GPA. From[START_REF] Johnson | a Protein Kinase Inhibitors: Contributions from Structure to Clinical Compounds[END_REF]

Figure 2 .

 2 Figure 2.01 Layout of optical components in a basic TEM

Figure 2 .

 2 Figure 2.02. Layout of negative staining method

Figure 2 .

 2 Figure 2.03. Layout of plunger

Figure 2 .

 2 Figure 2.04. Holey Carbon Grid emresolutions.com

Figure 2 .

 2 Figure 2.05. Typical images of the three forms of ice, at the same magnification of x10,000 (a) Hexagonal ice (b) Cubic ice and (c) Vitreous ice

Figure 2 .

 2 Figure 2.06. Contrast Transfer Function (From N. Boisset, unpublished lecture material) Left: The theoretical Contrast Transfer Function without the envelope function. Middle: The envelope function simulating the amplitude attenuation. Right: Contrast Transfer Function with (simulated) attenuation. Axis vertical: Contrast. Axis horizontal: Frequency.

Figure 2 .

 2 Figure 2.07. CTF with different defocus value. Image ME obtained by inverse Fourier transform of the product of the CTF and the Fourier transform of the original image. (From N. Boisset, unpublished lecture material) Row upper: CTF with a defocus value (defocus 1) where the position of the first zero is at 11Å. Row lower: CTF with a defocus value (defocus 2) where the position of the first zero is at 17Å.

Figure 2 .Figure 2 .

 22 Figure 2.08 Computed power spectra for a number of cryo-EM micrographs in the defocus range 1.2-3.9 μm.[START_REF] Frank | Three-Dimensional Electron Microscopy of Macromolecular Assemblies[END_REF]

Figure 2 .

 2 Figure 2.10. Plots of CTF for a range of defocuses, and a darker line representing the average of the individual curves.This darker line represents the shape of the filter that is effectively applied to a single particle reconstruction when CTF amplitude correction is not applied.[START_REF] Cong | Single Particle Analysis at High Resolution[END_REF]

Figure 2 .Figure 2 .

 22 Figure 2.11. Demonstration of Wiener filtering (From N. Boisset, unpublished lecture material)

Figure 2 .

 2 Figure 2.12b. Translational alignment of movie frames to reduce blurring in images affected by beam-induced motion. (A) Average of 60 frames of an area of Movie S1 that experienced translations of about 60 Å (see Fig. 3L) Particles are significantly blurred and high-resolution information is lost. (B) Average of the same 60 frames as in panel A after translational alignment of individual frames. The translations for the individual frames were calculated from the translations determined in Fig.3for the 10-frame averages by linear interpolation. The frame average in panel B exhibits substantially improved contrast with details at higher resolution that were not visible in panel A.

Figure 2 .

 2 Figure 2.12. Analysis of motion-induced image blurring on resolution of the 3D reconstruction. (a) Comparison of Fourier shell correlation (FSC) curves from 3D reconstructions using images without motion correction (red), images corrected using the entire subframe and containing all subframes (purple) and images corrected by subregion and containing subframes 3-15 (blue). Horizontal dashed lines are shown for both the FSC -0.5 and 0.143 criteria. The FSC curve between the final map and that calculated from the fitted atomic model is shown in orange. (b) Comparison of rotational averages of Fourier power spectra of the different 3D reconstructions.[START_REF] Li | Electron Counting and Beam-Induced Motion Correction Enable near-Atomic-Resolution Single-Particle Cryo-EM[END_REF]

Figure 2 .

 2 Figure 2.13. Euler angles

Figure 2 .

 2 Figure 2.14. Back-projection method.[START_REF] Frank | Three-Dimensional Electron Microscopy of Macromolecular Assemblies[END_REF]

Figure 2 .

 2 Figure 2.15. Principle of random conical tilt series method. A) Image series at 0°, represent top views of the object, with different in plan rotations, should be in the same class after alignment and classification. B) Image series at 45°, represent tilted views of those top view, respectively. C) 1-8: projective directions of tilted images. (From N. Boisset, unpublished lecture material)

Figure 2 .

 2 Figure 2.16. Demonstration of the missing cone in random conical tilt method A, B, C) Volume reconstructed in real space. D, E, F) Sampling of tilted image in reciprocal space, respectively. A) Volume with artifact from one class of top view. B) Volume with artifact from one class of side view. C) The combined volume from different classes to recover information from missing cones. (From N. Boisset, unpublished lecture material)

Figure 2 .

 2 Figure 2.17. Demonstration of the projection theorem and its use in 3D reconstruction (Lake 1971)

  genome. Human chromosomes are constantly exposed to a number of insults and challenges, originating from both intra-and extra-cellular sources. Rapid and effective cellular responses to these diverse genotoxic stresses are essential to ensure genomic stability. In this project, in collaboration with our collaborators in Oxford (the team of Martin Cohn, Biochemistry Department) we are interested in understanding how the various DNA repair pathways function in human cells. Cohn's laboratory previous work has lead to the discovery of novel proteins participating in DNA repair pathways such as the Fanconi Anemia DNA repair pathway. Interestingly, our findings demonstrate that these proteins often function as part of large multisubunit protein complexes. Using a combination of molecular biology, cell biology, biochemistry, and cryo electron microscopy we have identified and studied new proteins playing key roles in human DNA repair pathways.

Figure 3 .

 3 Figure 3.1 A: Coomassie blue stain of recombinant full-length hFANCD2/FANCI heterodimer purified from Sf9 cells. B: FANCD2/FANCI heterodimer negatively stained and imaged

Figure 3

 3 Figure 3.2 Full-length hFANCD2/FANCI complex (A) CryoEM density map of full-length hFANCD2/FANCI complex in different orientations. (Scale bar: 20 Å) (B) Comparison of full-length hFANCD2/FANCI CryoEM structure (left) with the mouseFANCD2/FANCI crystal structure (PDB: 3S4W) (right). The mFANCD2/FANCI crystal structure was filtered to the same resolution as the 3D Cryo-EM model. (Scale bar: 20 Å) (C) Different orientations of CryoEM density map of full-length hFANCD2/FANCI complex docked with the mFANCD2/FANCI crystal structure (PDB: 3S4W). (Scale bar: 20 Å)

Figure 3 . 3

 33 Figure 3.3 Truncated hFANCD2-ΔTower/FANCI complex and comparison with fulllength hFANCD2/FANCI complex (A) Schematic of FANCI and FANCD2 indicating the thumb-like domain and tower domain. (B) Coomassie blue stain of recombinant full-length hFANCD2/ FANCI and hFANCD2-ΔTower/FANCI heterodimer purified from Sf9 cells. (C) Different orientations of CryoEM density map of the hFANCD2-ΔTower/FANCI complex. (Scale bar: 20 Å) (D) Different orientations of CryoEM density map of full-length hFANCD2/FANCI complex (in blue mesh) superimposed with the hFANCD2-ΔTower/FANCI complex (in gold). (Scale bar: 20 Å)

Figure legends Figure 1 .

 legends1 Figure legends

Figure 2 .Figure 3 .

 23 Figure 2. A) Cryo-EM density map of full-length hFANCD2/FANCI complex in different orientations. (Scale bar: 20 Å) B) Comparison of full-length hFANCD2/FANCI Cryo-EM structure (left) with the mFANCD2/FANCI crystal structure (PDB: 3S4W) (right). The mFANCD2/FANCI crystal structure was filtered to the same resolution as the 3D Cryo-EM model. (Scale bar: 20 Å) C) Different orientations of Cryo-EM density map of full-length hFANCD2/FANCI complex docked with the mFANCD2/FANCI crystal structure (PDB: 3S4W). (Scale bar: 20 Å)

Figure 4 .

 4 Figure 4. A) Clonogenic assay of HeLa.Scramble, HeLa.shFANCD2 and HeLa.shFANCD2 complemented with EGFP tagged wild type, K561R, ΔTower and ΔTower-NLS FANCD2. B) FANCD2 monoubiquitination response in PD20 cells complemented with wild type FANCD2 and hFANCD2-ΔTower after 160ng/ml MMC overnight treatment. C) HeLa.shFANCD2 cells expressing EGFP-tagged wild type, ΔTower-NLS and K561R FANCD2 were pre-treated with TMP, and microirradiated at the indicated area (white arrows). Wild type FANCD2 were recruited to TMP induced ICLs sites, but not ΔTower-NLS and K561R FANCD2. Quantifications of the EGFP tagged FANCD2 at the irradiated sites are shown at the right. Scale bar: 10μm.

Figure 5 .

 5 Figure5. A) EMSA showing full-length FANCD2/FANCI complex forms specific protein-DNA complexes with replication fork substrate ICL14FF, but not the FANCD2-ΔTower/FANCI complex. B) EMSA showing full-length FANCD2/FANCI complex forms stronger specific protein-DNA complexes with crosslinked replication fork substrate ICL14FF-XL, but not the FANCD2-ΔTower/FANCI complex. C) Titration of protein concentration of full-length FANCD2/FANCI complex and FANCD2-ΔTower/FANCI complex in EMSA with replication fork substrate ICL14FF (left) and crosslinked replication fork substrate ICL14FF-XL. D) In vitro ubiquitination assay of full-length FANCD2/FANCI complex and FANCD2-ΔTower/FANCI complex. E) Schematic of patient derived mutations in the tower domain that led to deletion of the tower domain. F) Model showing how the FANCD2/FANCI is recruited to crosslinked replication fork, which precedes the monoubiquitination of the FANCD2/FANCI complex.

Figure S1 .

 S1 Figure S1. A) Micrograph of negative staining EM full-length FANCD2/FANCI complex 0° tilt. B) Tilted micrograph (50° tilt) of the same area in (A).

Figure S2 .

 S2 Figure S2. A) Micrograph of Cryo-EM full-length hFANCD2-ΔTower /FANCI complex. B) Different orientations of Cryo-EM density map of hFANCD2-ΔTower /FANCI complex docked with the mFANCD2/FANCI crystal structure deleting 1144 -1450a.a. (PDB: 3S4W)

Figure below :

 below Figure below: Micrograph examples for PhK: Original image, low pass at 4.5Å image, low pass 9Å image, low pass 18Å image. A Butterworth filter is used here.

Figure 4 .

 4 Figure 4.01. Cryo-EM micrograph examples averaged from 31 frames. Low-pass filtered at 9A. Pixel size: 1.237Å. Image size: 3710x3710 pixels. Low pass filter: Butterworth filter. Left: Original cryo micrograph averaged from 31 frames. Right: Same micrograph with aligned frames by motion correction.

  Figure below shows 2D Classification results. 48 Classes, reference free 2D classification in RELION, 26516 Particles, CTF corrected, box size: 288 pixels reduced to 144 pixels, particle size.

Figure 4 .

 4 Figure 4.03. Cryo-EM particle examples: Original, low pass filtered at 9 Å, low pass filtered at 18 Å. Particle example averaged from 31 frames. Pixel size: 1.237Å. Image size: 288x288 pixels. Low pass filter: Butterworth filter. Column left: Original non active PhK cryo micrograph Column middle: Same micrograph low pass filtered at 9Å Column right: Same micrograph low pass filtered at 18Å

Figure 4 .

 4 Figure 4.04. Reference free 2D classification results in RELION: Particles: 26516. Number of Classes: 48. CTF corrected. Number of iterations: 25. Angular sampling: 5 degrees. Translational search range: 48 pixels. Translational search step: 6 pixels. Box size: 288 pixels reduced to 144 pixels.

Figure ( 4 .

 4 Figure (4.06) below shows the comparison between two significant PhK particles and 2D class average they belong to. We can validate our classification that way.

Figure 4 .Figure 4

 44 Figure 4.05. Significant 2d class averages in the reference free 2d classification results in RELION: Left: Significant 2d class average, probably correspond to a Chalice view (Figure 4.07 row 1) Right: Significant 2d class average, probably correspond to a Cubic view (Figure 4.07 row 2)

Figure 4 .

 4 Figure 4.07. Three different starting models Column A: Active PhK negative staining model -(Model 1) RCT method (Vé nien et al., 2002) Column B: Non active PhK cryo-EM model -(Model 2) (Nadeau et al., 2005) Column C: Non active PhK SPARX ab initio cryo-EM model -(Model 3) Row 1) 2) 3) 4): Enzyme in different views. Row 5): After low pass filtering.

Figure 4 .

 4 Figure 4.11. 3D classification (K=3) result Class001 (in green) against its starting model -(Model 2) the non active PhK cryo-EM model (meshing in blue)

Figure 4 .

 4 Figure 4.15. Volume of Class3 (in blue) against its starting model -(Model 3) the non active PhK cryo-EM Sparx ab initio model (meshing in yellow). Row A: Class3, 3d classification resulting map, (Figure 4.14 column 3) Row B: Superposed A and C Row C: The non active PhK cryo-EM Sparx ab initio model -(Model 3, meshing in yellow) (1)(2)(3)(4) Enzyme views in different directions

Figure 4 .Figure 4 .Figure 4 .

 444 Figure 4.16. Volume of Class3 (in red) against its starting model -(Model 1) the active PhK negative stained model (meshing in blue). Row A: Class3, 3d classification resulting map, (Figure 4.13 column 3) Row B: Superposed A and C Row C: The active PhK negative stained model -(Model 1, meshing in blue) (1)(2)(3)(4) Enzyme views in different directions

Figure 4 .

 4 21 below shows the result of 3D classification from 26516 particles with K=1 (one class) and starting Model 1.

Figure 4 .

 4 Figure 4.21. One-class (K=1) 3D classification results by using 26516 particles, with reduced image: 2.474Å/pixel. Upper row: resulting volume (in grey) at a resolution of 12.72Å, with 155 iterations. Lower row: resulting volume (in pink) at a resolution of 9.137Å, with 279 iterations.

Figure 4 .

 4 Figure 4.22. Two-classes (A and B, K=2) 3D classification results from 26516 particles, with reduced image: 2.474Å/pixel. 3D classification continued from the preceding one-class 3D classification. Upper row: resulting volume of Class A (in purple) at a resolution of 13.7Å after 38 iterations. Lower row: resulting volume of Class B (in pink) at a resolution of 13.7Å after 38 iterations.

After 155 - 4

 1554 279 iterations, the one-class (K=1) 3D classification produced a volume (Figure 4.21), which is very similar to the converged volumes "Class3" (Figure. 4.18) at a resolution level of 12Å. The significant structural features (two bridges linking two lobes with four peaks on each lobe) have also appeared. The two resulting classes (A and B Figure 4.22) from subsequent two-classes 3D classification look much more similar to each other (at a resolution of 13.7Å) than the three resulting classes 1)2)3) in the previous three-classes (K=3) 3D classification (Figure By adding newly selected particles, we have tried one, two, three and five-classes 3D classification to prove that the converged maps (Figure 4.18 red blue; Figure 4.21 grey pink) are still meaningful for all selected particles (33319 particles) data set. The figure below shows the two-classes (K=2) from 3D classification from 33319 particles.By analyzing all 3D classification results with 33k particles, we can see that there is always a class (in green, Figure4.23 row A and Figure4.24 row B) with its map similar to the converged maps "Class3" (Figure4.18 blue and red). And it's the only one, which has a very clear bridge region connecting the two lobes (all maps have a volume value corresponding to the mass of the enzyme: 1.3M Dalton).The figure below shows the three-classes (K=3) from 3D classification from 33319 particles.

Figure 4 .

 4 Figure 4.23. Two-classes 3D classification result by using all 33319 particles (K=2): With reduced image: 2.474Å/pixel. T=4. Angular sampling: 1.8 degree. Translational sampling: 1 pixel. Resolution: 12.72 Å. Iterations: 89. Row A: resulting map of class001. Particle distribution: 47.8%. Row B: resulting map of class002. Particle distribution: 52.2%. B

Figure 4 .Figure 4 .

 44 Figure 4.25. 3D classification and 3D refinement results with normal averaged particles, only spherical mask is used. 9280 particles, pixel size: 1.237Å/pixel. Row Upper: Density map of one-class (K=1) 3D classification result (in green) at a resolution of 8.03Å, same as Figure 4.20 Row Lower: Density map after 3D refinement step with normal averaged particles (in grey), at a resolution of: 11.32Å. All maps have a volume value corresponding to the mass of the enzyme: 1.3M Dalton.45°90° 90°

Figure 4 .Figure 4 .Figure 4 .Figure 4 .Figure 4 .

 44444 Figure 4.26b. Fits straight lines through movement tracks in Figure 4.26a.

Figure 4 .

 4 Figure 4.31. 3D Re-refinement results with polished 9280 particles, and sharpened density map, pixel size: 1.237Å/pixel. Row upper: density map after re-refinement step with the polished particles (in red), resolution: 10.18Å (Figure 4.30 FSC-curve [5] in blue). Row lower: density map after post-processing (Sharpening) step (in purple), B-factor: automatic estimation, low pass filtered according to the mask-corrected, gold standard FSC-curve. All maps have a volume value corresponding to the mass of the enzyme: 1.3M Dalton.

Figure 4 .

 4 Figure 4.19 Figure 4.20) with 9280 particles. In this chapter we performed the same refinement steps as for the 9280 particles but with 15986 particles total.

Figure 4 .Figure

 4 Figure 4.34 3D classification, 3D refinement, and post processing results with normal averaged particle images based on one of the two resulting classes of the twoclasses 3D classification (Figure 4.23 row A in red), 15986 particles, and pixel size: 1.237Å/pixel. Row A: 3d classification result, T=4, iteration: 227, resolution: 6.98Å. Row B: 3d refinement result, T=3, resolution: FSC-0.5 = 11.5Å, FSC-0.143 = 7.581Å, accuracy rotations: 1.498, accuracy translations: 1.838 Row C: Post processed result (sharpened density map), resolution: 7.272Å.

Figure 4 .

 4 Figure 4.35 Fourier Shell Correlation curves from 3d refinement result. 15986 normal averaged particles, and pixel size: 1.237Å/pixel. Curve in blue: FSC-curve of 3d refinement resulting map (Figure 4.34 row B). Resolution (1/Å): FSC-0.5: 0.0869 = 11.5Å, FSC-0.143: 0.1319 = 7.581Å resolution

Figure 4 .Figure 4 .

 44 Figure 4.38. Re-refinement, and post processing results with polished particle images based on the result of 3d refinement with normal averaged particles (Figure 4.34 row B in red), 15986 particles, and pixel size: 1.237Å/pixel. Row A:Sharpened density map of Re-refinement result (the same map in Figure4.36 row B but with a higher threshold), B-factor automatic estimation: -53, low-pass filtered according to the mask-corrected, gold standard FSC-curve. Resolution: 6.85Å.Row B:Sharpened density map of Re-refinement result (with the same threshold in row A), B-factor: -100, low-pass filtered according to the mask-corrected, gold standard FSC-curve. Resolution: 6.85Å.Row C:Chimera solid mode of the density map in row A.

Figure 4 . 2 .

 42 Figure 4.40 Reconstructed inactive PhK cryo-EM structure, chalice view.

Figure 4

 4 Figure 4.43 Reconstructed inactive PhK cryo-EM structure, a side view.

Figure 4 .

 4 Figure 4.45 Comparison of 3D maps between non-activated PhK and Ca2+ activated PhK at a resolution level of 10 Å. (solid and transparent maps)

  By comparing the non-active PhK structure with the active PhK structure at a resolution of 10Å (Figure 4.44 and 4.45), we found that: 1) The non-active PhK is smaller and more compact. Contrarily the active PhK has a more open structure. 2) There is another hole (axial) through the active PhK, but the non-active PhK doesn't have it (Figure 4.45 lower row). 3) The shape of the hole formed by the bridges are different between two conformation states (Figure 4.44 A(2) C(

Acknowledgements

The authors would like to thank members of the Cohn laboratory for reading and discussing the manuscript, and Helen Walden for help with establishing the in vitro monoubiquitination assay. We are grateful to staff at the Micron microscopy facility for excellent help and support. This work was supported by grants UF100717 and ALRXNV0 from the Royal Society (M.A.C.), grant 103/789 from The Fell Fund (M.A.C.), grant C5255/A18085 from Cancer Research UK (CR-UK) through the CRUK Oxford Centre (M.A.C.), the Taiwanese Government (C.C.L) and a Goodger Scholarship (C.C.L), BBSRC grant (WN and CVB).

in RELION [START_REF] Scheres | RELION: implementation of a Bayesian approach to cryo-EM structure determination[END_REF], with 25 iterations of 1 class and 4 classes 3D classification. The RCT 3D reconstruction from negatively stained full length FANCD2/FANCI was used as the starting model. The final resolution is about 22Å.

For the C-terminus deleted FANCD2/FANCI, frozen particles were selected using Roseman's algorithm (Roseman, 2003) in a SPIDER (Frank et al., 1996) procedure, with the previous frozen hydrated full-length FANCD2/FANCI structure, and then manually screened using the EMAN (Ludtke, Baldwin, and Chiu 1999) program Boxer. 8547 particles were selected from 71 micrographs with a defocus range of 3-5.2 μm. Defocus was determined using CTFFIND3 (Mindell and Grigorieff, 2003). Refinement was carried out using EMAN (Ludtke, Baldwin, and Chiu 1999) software package. The frozen hydrated full-length FANCD2/FANCI structure was used as the starting model and refined against the C-terminus deleted FANCD2/FANCI particles. At each iteration, particles with bad cross-correlation values were temporally removed, and intermediate volume was band-pass filtered between 10 and 150 Å. For the resolution limit estimation of 3D-reconstruction volumes, two independent reconstructions were carried out and compared in reciprocal space using increasing shells with the FSC (Fourier shell correlation) technique. Visualization of 3D density map was done in UCSF Chimera (Pettersen et al., 2004).

Structure of the full length human FANCD2/FANCI heterodimer

The highly pure complex (Figure 3.1A) was then subjected to electron microscopy analysis using negative stain. Representative micrographs of the grids demonstrated homogeneity of the sample (Figure 3.1B) 4082 pairs of particles were used to calculate a 3D model using the random conical tilt method (Radermacher et al., 1987).

Manuscript

Cryo-EM structure of the human FANCD2/FANCI complex reveals a novel Tower domain required for FANCD2 monoubiquitination.

Chih-Chao Liang 1 , Zhuolun Li 2 , William V. Nicholson 1,2 , Catherine Vé nien-Bryan 1,2 and Martin A. Cohn 1,* 1 Department of Biochemistry, University of Oxford, Oxford, OX1 3QU, United Kingdom. 2 IMPMC UMR7590, University Paris 06, F-75252, Paris, France.

Summary

The Fanconi Anemia (FA) pathway has been implied to play a significant role in DNA interstrand crosslink repair and may be the coordinator between different DNA damage repair pathways such as homologous recombination and nuclear excision repair. Within the FA pathway, the FANCD2 and FANCI proteins are key players essential for the functionality of the pathway. Neither of the proteins possesses any known conserved domains, and they are suggested as paralogs of each other. Upon genotoxic stress, both proteins are monoubiquitinated by the FA core complex, and quickly recruited to sites of DNA damage together with other DNA repair proteins. Yet, the molecular mechanism of how the two proteins coordinate the DNA repair responses remains elusive. Here we show the first structural insight into the human FANCD2/FANCI complex by obtaining the cryo-EM structure of the complex of full-length proteins. The complex appears with an inner cavity, large enough to accommodate a double stranded DNA helix. We also discovered a protruding Tower domain, which we have shown to be critical for the recruitment of the complex to ICLs in vitro and in vivo, and for the monoubiquitination of FANCD2. Disease-causing mutations in this domain is observed in several FA patients. We demonstrate that the complex binds strongly to a DNA structure resembling a replication fork stalled at an ICL. Finally, our work reveals that recruitment and binding of the complex to a stalled replication fork serves as the trigger for the activating monoubiquitination event. Taken together, our results uncover the mechanism of how the FANCD2/FANCI complex activates the FA pathway, and explains the underlying molecular defect in FA patients with mutations in the Tower domain.

Protein purification

FANCD2 and UBA1 are expressed using the pFastBac1 vector (Life Technologies) with an engineered N-terminal Flag-HA tag, and FANCL is expressed using the pFastBac1 vector (Life Technologies) with an engineered N-terminal Flag-MBP tag. UBE2t is expressed and purified as described (Hodson et al., 2014). For FANCD2/FANCI complex, Sf9 cell pellets were resuspended in lysis buffer (20 mM Tris-HCl pH 8.0, 0.1 M KCl, 10% glycerol and 0.2 mM PMSF), and sonicated. Lysates were clarified by centrifugation, and the supernatants were incubated with M2 anti-FLAG agarose resin (A2220, Sigma) for 2 hr. The resin was washed carefully, and the protein was eluted in the same buffer containing 0.5 mg/ml FLAG peptide. The flag eluate was supplemented with 20mM Imidazole and incubated with Ni 2+ -NTA (30310, QIAGEN) at 4 degree for 2 hour with rotation. The resin was washed carefully, and eluted with buffer containing 20mM Tris (pH 8.0), 0.1M KCl, 250mM Imidazole, 0.2mM PMSF and 10% glycerol. The Ni 2+ -NTA eluate was injected into pre-equilibrated size exclusion chromatography column, Superdex 200, and eluted with the base buffer containing 20mM Tris (pH 8.0), 0.1M KCl and 5% glycerol. The FANCD2-ΔTower/FANCI complex is purified as full-length FANCD2/FANCI except the size exclusion chromatography step. UBA1 and FANCL are purified against FLAG tag described as above.

Electron microscopy and 3D image processing

Negatively stained freshly prepared full length FANCD2/FANCI was applied to glowdischarged, carbon-coated grids and allowed to adsorb for 15-60 s. Specimen was then stained with 2% uranyl acetate. Vitrified full length FANCD2/FANCI and C-terminus deleted FANCD2/FANCI was prepared on glow discharged carbon quantifoil grids. Specimen were imaged at a nominal magnification of 30,000× with a 2k×2k Gatan CCD camera (corresponding to a pixel size of 3.5 Å at the specimen level) in a JEOL 2100, LaB6 operating at 200kV. A preliminary full length FANCD2/FANCI 3D model was calculated using the random conical tilt method (RCT) (Radermacher et al., 1987) and the WEB and SPIDER software package (Frank et al. 1996). For this first 3D model, 4082 pairs of particles of full length FANCD2/FANCI were picked from 80 tilt pair images recorded at 50° and 0°.

Refinement of the 3D volume obtained from the RCT method was done using images from frozen hydrated full length FANCD2/FANCI. 5058 particles were selected after using Roseman's algorithm (Roseman, 2003) in SPIDER (Frank et al., 1996) procedure and subsequently manual selection in WEB (Frank et al., 1996). Defocus was determined using CTFTILT (Mindell and Grigorieff, 2003). 3D classification and refinement were carried out in RELION [START_REF] Scheres | RELION: implementation of a Bayesian approach to cryo-EM structure determination[END_REF], with 25 iterations of 1 class and 4 classes 3D classification. The RCT 3D reconstruction from negatively stained full length FANCD2/FANCI was used as the starting model.

For the C-terminus deleted FANCD2/FANCI, frozen particles were selected using Roseman's algorithm (Roseman, 2003) in a SPIDER (Frank et al., 1996) procedure, with the previous frozen hydrated full-length FANCD2/FANCI structure, and then manually screened using the EMAN (Ludtke et al., 1999) program Boxer. 8547 particles were selected from 71 micrographs with a defocus range of 3-5.2 μm. Defocus was determined using CTFFIND3 (Mindell and Grigorieff, 2003). Refinement was carried out using EMAN (Ludtke et al., 1999) software package. The frozen hydrated full-length FANCD2/FANCI structure was used as the starting model and refined against the C-terminus deleted FANCD2/FANCI particles. At each iteration, particles with bad cross-correlation values were temporally removed, and intermediate volume was band-pass filtered between 10 and 150 Å. For the resolution limit estimation of 3D-reconstruction volumes, two independent reconstructions were carried out and compared in reciprocal space using increasing shells with the FSC (Fourier shell correlation) technique. Visualization of 3D density map was done in UCSF Chimera (Pettersen et al. 2004).

Preparation of interstrand crosslinked DNA substrates

The DNA oligos were annealed in the buffer containing 10 mM Tris-HCl pH7.5, 100 mM NaCl and 1 mM EDTA. 4,5′,8-trimethylpsoralen (TMP, Sigma, T6137)/UVA (365 nm) crosslinking induction was described previously (Esposito, Brankamp, and Sinden 1988).

Interstrand crosslink was confirmed by 8M urea 12% denaturing polyacrylmide gel electrophoresis. 3D classifications were carried out with the number of classes K = [1,2,3], which means the 3D multi-reference classification with 1,2,3 references respectively. The regularization parameter T (Scheres 2012b) that may put slightly more weight on the experimental data was set to 3 initially. The symmetry was set to D2.

3D classification from model 1

By comparing the results of 3D classifications, with K=1,2,3 and the starting model 1, we found one common structure, with two bridges connecting two lobes, and four peaks on each lobe (Figure 4.08 A(1), B(1), C( 2)).

Then one of the resulting volumes (K=1, Figure 4.08 A(1)) was picked, as it contains all 26516 particles and has a less detailed volume surface and compared with its starting model by superposing them (Figure 4.09).

(1)

(2) (3) Likewise, at the exterior side of the lobe, two of the four observed peaks were outside the starting model (Figure 4.09 row B). This could be explained by the potential ( ( The next chapter investigates the 3D refinement, movie processing, and B-factor sharpening using 9280 particles from the "Class3" (Figure 4.18 red; Figure 4.20 Green), with which we hope to get a structure at higher resolution. In this project, for the 2D and 3D classifications, a circle soft mask at a diameter of 351Å is used for the particle images to reduce noise, and a soft spherical mask with the same diameter is used on the 3D reference. For higher resolution 3D refinement, the solvent area of the particle images is filled with random noise, since the pattern of circle mask may introduce correlations while particle images are compared with the 2D projections of 3D reference. In addition the 3D refinement resulting map is masked in the post-processing step with an automatically generated mask adapted to the 3D map.

A recent paper working at the spliceosomal U4/U6.U5 tri-snRNP [START_REF] Nguyen | The Architecture of the Spliceosomal U4/U6.U5 Tri-snRNP[END_REF], a big enough macromolecule (1.5 mega Dalton) with low symmetry C1 (D2 for PhK), using the same RELION protocol, shows that there may be some flexible parts, which disturb the orientation searching and reduce the resolution of the resulting map. The solution they used to overcome this problem is to mask out the flexible parts in the refinement step to achieve a better resolution for the rigid parts in the mask. This kind of mask could be used if some flexible part of the PhK were found in the further image processing.

Auto-refinement in RELION

This first test is based on one of the two converged "Class3" density maps (Figure 4.18 Figure 4.19 Figure 4.20) with 9280 particles. A normal 3D refinement had been done

with particles selected from frame-averaged micrographs (Figure 4.25). As we know from the recent study coming out with the Direct Electron Detection Camera that the recorded movies should be corrected to avoid beam-introduced motion of the specimen. A global frame alignment step has been done before calculating the frame-averaged micrograph and the subsequent particle selection by using "MotionCorr" [START_REF] Li | Electron Counting and Beam-Induced Motion Correction Enable near-Atomic-Resolution Single-Particle Cryo-EM[END_REF].

In this project, each micrograph was recorded with 30 frames (1.94 e-/Å 2 /frame). And so far the 21 frames (from the 1 st to the 21 th ) were used in the movie-processing step. In addition, the images analysis performed so far where done with an initial frame alignment (MotionCorr software see chapter 4.1.1). In order to gain more structural information at high resolution and because we noticed that this correction was not sufficient we decided to test the polishing method in RELION for a better alignment of the particles.

We can see that the first 5-6 frames have a low linear intensity factor, which could be explained by the high level motion at the head of the movies. Since the beam introduced motion is more important and macromolecule move faster in the first 5-6 frames.

Re-refinement with the polished particles

The polished particles have increased signal-to-noise ratios compared to the original ones. So a re-refinement step has been done with the polished particles (weighted averages).

The resulting map before and after this step is shown in figure 4.29 row A B. And we saw a significant improvement of the density map. By comparing the Masked and Unmasked FSC curves [6]- [4] [3]- [2], we can also see a significant amelioration of resolution.

region and the extremity of the alpha sub-unit there is a thin-arm like structure (Figure 4.31 black arrow).

3D Classification and 3D Refinement after Movie Processing

Further 3D classification and 3D refinement with polished particles have also been tried. The difference between this step and those 3D classifications in section 4.1.5 is that we are using locally motion corrected and weighted averaged (polished) particles and only 21 frames of the 30 are included.

One-class 3D classification with the same 9280 particles are shown below each color indicated a different number of iteration.

Then we performed more 3D refinement by using the result of one-class 3D Abstract : During my thesis work, I have investigated the structure of two protein complexes, the FANCD2/FANCI complex and the Phosphorylase Kinase complex (PhK). Both complexes were studied using cryo electron microscopy combined with image analysis.

The Fanconi Anemia (FA) pathway has been implied to play a significant role in DNA interstrand crosslink repair and may be the coordinator between different DNA damage repair pathways. Within the FA pathway, the FANCD2 and FANCI proteins are key players. In my thesis work, I have calculated the structure of the human FANCD2/FANCI complex. It possesses an inner cavity, large enough to accommodate a double stranded DNA helix. We also discovered a protruding tower domain, which our collaborator (M. Cohn, Oxford) has shown to be critical for the recruitment of the complex to DNA. PhK is one of the most complex kinases. It is composed of four subunits (αβγδ)4. PhK regulates glycogenolysis, it integrates various signals to catalyze the conversion of glycogen phosphorylase (GP) b to GP a (active), and the subsequent breakdown of glycogen. PhK is a potential target for glycemic control in diseases such as diabetes. Using state of the art electron microscope with a direct electron detection camera, after multiple image processing steps and correction of beams induced motion of films, I obtained a structure of the complexe at 7Å (FSC gold standard).