P. pastoris, an important model organism and common expression system used in protein production. From these data, we determined the structure of P. pastoris FAS to 3.1 Å resolution. While the overall organisation of the complex was typical of type I FASs, we identified several differences in both structural and enzymatic domains through comparison with the prototypical yeast FAS from S. cerevisiae. Using focussed classification, we were also able to resolve and model the mobile acyl-carrier protein (ACP) domain, which is key for function. Ultimately, the structure reported here will be a useful resource for further efforts to engineer yeast FAS for synthesis of alternate products.Type I fatty acid synthases (FASs) are critical metabolic enzymes which are common targets for bioengineering in the production of biofuels and other products. Serendipitously, we identified FAS as a contaminant in a cryoEM dataset of virus-like particles (VLPs) purified from  In addition to their key physiological importance, microbial FAS systems are also a common target of metabolic engineering approaches, usually with the aim of generating short chain fatty acids for an expanded repertoire of fatty acid-derived chemicals, including chemicals with key industrial significance such as α-olefins6. A comprehensive structural and functional understanding of a diverse range of FAS systems is therefore paramount to the success of such complex rational engineering approaches.Fatty acid synthases (FASs) are critical metabolic enzymes for the endogenous biosynthesis of fatty acids in a diverse range of organisms. Through iterative cycles of chain elongation, FASs catalyse the synthesis of long-chain fatty acids that can produce raw materials for membrane bilayer synthesis, lipid anchors of peripheral membrane proteins, metabolic energy stores, or precursors for various fatty acid-derived signalling compounds7, Type I FAS systems in fungi and animals are large complexes that integrate the key enzymatic activities required for fatty acid biosynthesis9. In particular, yeast type I FASs are 2.6 MDa hetero-dodecameric complexes with an α6β6 configuration, which form a cage-like structure comprising two dome-shaped reaction chambers on either side of a central platform14. Along with structural support regions, enzymatic domains form the walls of the reaction chambers, with most active sites facing the chamber interior. The mobile acyl-carrier protein (ACP) domain tethers the growing fatty acid chain and carries it between active sites, so each can act on this in turn16. The phosphopantetheine transferase (PPT) domain, which catalyses the attachment of a phosphopantetheinyl group to the active site serine residue of the ACP (necessary for ACP activity), is also an integral part of yeast FAS complexes, and is located on the exterior of the cage18.Unlike the dissociated systems of individual enzymes that make up type II FAS systems in plants and bacteriaS. cerevisiae FAS revealed a number of differences with X-ray crystal structures of fungal FASs, suggesting that crystal contacts had altered the conformation of FAS from its unconstrained structure in solution14. A more recent attempt to determine the solution structure of S. cerevisiae FAS at a resolution sufficient for molecular modelling was hindered by partial denaturation induced by interactions with the air–water interface, an effect that was ameliorated by coating grids with hydrophilized graphene, ultimately leading to a 3.1-Å resolution structure when sample preparation was optimised20. More recently, the structure of S. cerevisiae FAS was determined at 2.8 Å resolution in complex with a previously unknown regulatory subunit21.An extensive body of work underlies our current structural understanding of yeast FAS, although FAS structure determination to date has been challenging. A cryoEM study of S. cerevisiae22. To date, no structures are available for Pichia pastoris FAS from either cryoEM or X-ray crystallography. This represents an important gap in our knowledge, because P. pastoris (Komagataella spp.) is a methylotrophic yeast which is used extensively for recombinant protein production23. In recent years, it has also been the target of metabolic engineering approaches24. P. pastoris has a major advantage over other yeast strains like S. cerevisiae, as it produces relatively little ethanol, making the maintenance of productive, high-cell density fermenter cultures more tractable25. It has also been suggested that P. pastoris cells, in contrast to cells of the plant Arabidopsis sp., are highly tolerant of free fatty acids26.While our understanding of yeast FAS structure is growing, our library of structures remains incomplete. The vast majority of published solution structures of FAS are from P. pastoris FAS, determined by cryoEM. We initially identified FAS as a contaminant in a P. pastoris-derived virus-like particle (VLP) preparation being used for structural studies, and processed these data to yield detailed structural information on FAS, including density for the mobile ACP domain. Comparison of our atomic model with that of S. cerevisiae revealed interesting structural differences throughout the complex within both structural and enzymatic domains. This included movement of the malonyl/palmitoyl transferase domain at the level of domain packing, and alternative rotamers in the catalytic centres of the acetyl transferase and enoylreductase domains.Here, we report the 3.1-Å resolution solution structure of 27 with a SUMO-binding affimer inserted into the major immunodominant region (MIR)28 were expressed in transformed P. pastoris cells for structural and vaccine studies (construct available upon request). Expression was induced with methanol and the VLPs purified from the yeast lysate by sucrose gradient centrifugation. Image analysis of cryoEM data revealed, in addition to the VLPs, non-VLP-like particles which were recognised as yeast FAS complexes due to their characteristic morphology—two hollow chambers on either side of a central platform. Further inspection of individual micrographs revealed that FAS particles were well-separated from VLPs, indicating that FAS was not co-purified as a result of an association with the VLPs  composed of hepatitis B virus tandem core proteinsLPs Fig. a.Figure 19, we saw no signs of denaturation in our data set, judged by 2D class averages S. cerevisiae FAS were used to generate a 4.0-Å resolution structure19. More recently, ~ 15,000 particles of S. cerevisiae FAS led to a 3.1-Å resolution map, although in this case the FAS was incubated with NADPH and malonyl-CoA before vitrification in order to drive synthesis of fatty acids to completion, increasing particle homogeneity20.Classes corresponding to FAS were taken forward and processed separately, yielding a 3.1 Å reconstruction from ~ 37,000 particles, with parts of the central platform resolved to ~ 2.7 Å Fig. . In compP. pastoris FAS reported here was typical of a yeast FAS. Density was present for most domains of the α- and β-subunits, including acetyl transferase (AT), ketoacyl reductase (KR), ketoacyl synthase (KS), enoylreductase (ER), dehydratase (DH) and malonyl/palmitoyl transferase (MPT) domains . However, the approximate position of the ACP could be identified within the reaction chamber. There was no density apparent for the PPT region, as discussed further below. Furthermore, we saw no indication of a γ subunit, as recently reported for S. cerevisiae FAS21.The overall structure of the ins Fig. a,b. ACP P. pastoris FAS. An initial homology model for the asymmetric unit was generated using SWISS-MODEL29 and rigid-body fitted into the density map. The fit of model to density was visually inspected and corrected, before the model was symmetrised and refined to improve its fit to the density and geometry. The α subunit is mostly resolved, though several segments of the peptide sequence (including the ACP and PPT domains) are missing . The β subunit is practically complete, lacking only a small number of residues at the N- and C-termini (residues 1–9 and 2064–2069). We also observed density for a ligand bound within the active site of the ER domain , a flexible structural element that contributes to the stability of the complex but is often missing from FAS structuresP. pastoris FAS, we carried out a comparative structural analysis. While PDBeFold32 suggested that the closest structural matches to P. pastoris FAS were cryoEM structures of C. albicans FAS 22, which shares 73% (α) and 68% (β) sequence identity with P. pastoris FAS, we chose to compare our structure with S. cerevisiae FAS, a better characterised FAS with greater relevance to metabolic engineering attempts. At the peptide sequence level, P. pastoris FAS α and β subunits show 69% and 63% identity with their equivalents in S. cerevisiae. We compared our structure with a recently published 3.1-Å resolution structure of FAS from S. cerevisiae (PDB 6TA1)20. After aligning the two structures, the RMSD value between equivalent Cα atom pairs was calculated as 7.1 Å, indicating a notable level of difference between the two models   by individual subunits gave RMSD values of 1.7 Å (α–α), and 2.2 Å (β–β), suggesting that this may be partially attributable to differences in the spatial relationship between the α and β subunits in each model.To identify unique structural features of 30) Fig. . HoweverP. pastoris FAS, opening up a cleft between the two subdomains close to the active site 20, with a root-mean-square deviation (RMSD) value of 2.1 Å between equivalent Cα atom pairs in the ACP domains after alignment based on the KS domains. This is in contrast to C. albicans FAS (PDB: 6U5V), where the ACP domain was observed to localise to the ER domain22.Visual inspection confirmed that several domains appeared shifted, particularly the MPT domain. Here, the ferredoxin-like subdomain is positioned further away from the α/β-hydrolase fold in ite Fig. . The P. S. cerevisiae yeast structure. Other loops and linker regions throughout the β subunit also showed obvious differences in conformation, including in the DH domain , MPT domain (A1859-Y1870), and structural domains/linker regions   Fig. c. IntereS. cerevisiae FAS . By processing this ‘contaminant’ data and using focussed 3D classification techniques, we were able to determine the structure of the P. pastoris FAS to 3.1 Å resolution, and build an atomic model for the complex, including a model for the mobile ACP domain.We serendipitously identified fatty acid synthase, which is constitutively expressed in yeast19. It is possible that the presence of VLPs in the sample affected the ice thickness or crowded the air–water interface (which is known to induce denaturation19), reducing any interactions between the interface and FAS. However, we suspect that the lack of partial denaturation is more likely a result of the continuous thin carbon film used for making these cryoEM grids, which may trap FAS particles away from the air–water interface. While the presence of a thin carbon film adds a background that reduces the signal-to-noise ratio of particles (and can therefore limit the resolution), we were able to resolve our map to 3.1 Å, equivalent to that of the structure reported by Joppe et al. (2020)20 and sufficient to build a molecular model for FAS. Therefore, using a continuous thin carbon film may be a simple alternative to coating grids with hydrophilized graphene, when the size of the target protein  is similar to FAS.Interestingly, we did not observe signs of partial denaturation of FAS in 2D class averages, as was seen previously for a different yeast FAS structure19 and the quality of the ACP domain density was relatively poor. In this case, density corresponding to the ACP domain was initially of insufficient quality for molecular modelling, but this was resolved by focussed classification. Given the known mobile nature of the ACP domain14, this was unsurprising. Focussed classification led to an improvement in the strength and quality of the ACP density that was sufficient to allow atomic modelling, probably as result of grouping together only subparticles with ACP domains in the same relative position  at the time of vitrification. Use of an expanded mask for focussed classification revealed classes with putative ACP density in other positions within the complex interior. These classes contained relatively few subparticles, suggesting that the vast majority of ACP domains were adjacent to the KS domain, with the remainder either missing or occupying other positions. Notably, the positioning of the ACP domain was different to that observed in FAS structures from C. albicans (PDB: 6U5V)22 and C. thermophilum (EMDB: 3757)34, where the ACP domain localised to the ER domain. This was suspected to be a result of weakened negative surface charge in the KS domain, given that the ACP-KS interaction is thought to be driven by surface electrostatics35. Interestingly, while P. pastoris FAS retains some of the key acidic residues involved in the ACP-KS interaction that are altered in C. albicans FAS, other key acidic residues conserved in both S. cerevisiae FAS and C. albicans FAS are replaced with non-charged residues  may be more important for driving the ACP-KS interaction.The overall structure was typical of a yeast FAS. As has previously been observed for yeast FAS structures, the PPT domain was missing20. Since our initial aim was to purify VLPs, not FAS, no special care was taken to ensure the complex was kept intact . While focussed classification was not sufficient to resolve the PPT domain, one class did show weak density corresponding to the PPT domain, suggesting that it was intact in at least some particles.The lack of density for the PPT domain may result from partial denaturation of this domain in most complexes, as has previously been suggestedP. pastoris FAS structure reported here and a recent 3.1-Å resolution structure from S. cerevisiae (PDB 6TA1)20, another member of the yeast family Saccharomycetaceae. The MPT domain was distinctly shifted relative to its position in S. cerevisiae FAS, including a change in the positioning of the ferredoxin-like subdomain relative to the α/β-hydrolase fold. This is of particular importance, because this domain is the target of one approach to engineer FAS complexes to produce short chain fatty acids4. Many intradomain differences affected structural (non-enzymatic) components of the complex, including a loop within the MPT domain from S1752 – K1768. This loop was particularly interesting, as very weak density tracing the backbone of the alternative (S. cerevisiae) conformation also became visible at low contour thresholds. This is indicative of a mixed population of conformations in P. pastoris FAS, with most complexes sampling the conformation modelled, and a smaller subpopulation sampling the conformation seen for S. cerevisiae FAS. This loop may plausibly be dynamic, an idea reinforced by (i) other EM and X-ray crystal structures of S. cerevisiae FAS showing the equivalent loop in ‘intermediate’ positions  was seen to be in a ‘flipped’ conformation relative to its equivalent in S. cerevisiae FAS20. Interestingly, the conformation reported here is much closer to that seen in a crystal structure of S. pneumoniae FabK in its active state (PDB 2Z6I), a homologous enzyme that forms part of its type II FAS system36. Whether these changes to active site residues have functional significance is unclear. However, it should be noted that the S. cerevisiae FAS was incubated with NADPH and malonyl-CoA prior to vitrification in order to drive FA synthesis to completion20. The P. pastoris FAS reported here received no such treatment and as such, reaction status was likely less uniform and potentially incomplete.We also identified differences in the active sites of two enzymatic domains. In the AT domain, Q171 forms part of a tight hydrogen bond network around the active serine (S282) and its backbone amide also contributes to an oxyanion hole, which plays a role in promoting the transfer of the acetyl moiety to the thiol group of the ACPP. pastoris FAS to 3.1-Å resolution and identify structural differences with a related yeast FAS. As well as highlighting the importance of considering ‘contaminant data’ in cryoEM data sets, the structure reported here could prove a useful resource for future efforts to engineer yeast FAS.In conclusion, despite non-optimised sample preparation and using just ~ 37,000 particles, we were able to determine the structure of FAS was purified from PichiaPink™ yeast strain one , which was grown according to the manufacturer’s instructions.33, our initial aim was to use yeast to express and purify VLPs composed of hepatitis B virus tandem core proteins27 with a SUMO-binding affimer28 inserted into the major immunodominant region (MIR) (construct available upon request) for structural characterisation. As such, VLP expression was induced in the transformed yeast prior to purification, as described previously37. Briefly, glycerol stocks known to exhibit good levels of expression were cultured in yeast extract-peptone-dextrose (YPD), supplemented with 50 µg/mL ampicillin, at 28 °C, 250 rpm for 48 h. 2 ml of the high-density starter culture was then transferred to 200 mL YPD and incubated for another 24 h under the same conditions. To induce expression of VLPs, cells were pelleted  and resuspended in 200 mL yeast extract-peptone-methanol  for a further 48-h incubation, with methanol added to a final concentration of 0.5% (v/v) 24 h post-induction. Cells were subsequently collected by pelleting at 2000×g (20 min), resuspended in 30 mL breaking buffer  and stored at − 20 °C prior to purification.While the focus of this work was FAS, which is constitutively expressed in yeast37) for structural characterisation, FAS co-purified with the particles. Frozen P. pastoris stocks that had been induced to express VLPs were thawed and incubated with 0.1% (v/v) Triton-X100 for 30 min. Cells were lysed using a CF-1 cell disruptor at ~ 275 MPa and cooled to 4 °C, before the lysate was centrifuged first at 4000 rpm , then at 10,000×g  to remove insoluble material. The supernatant was supplemented with 2 mM MgCl and treated with HS-nuclease (2BScientific) for 1.5 h at room temperature, then mixed with saturated ammonium sulphate solution (40% [v/v]) and incubated overnight at 4 °C for precipitation. Precipitated protein was recovered by pelleting at 4000 rpm  and solubilised by resuspending in PBS (supplemented with 0.1% [v/v] Triton-X100). After further centrifugation at 4000 rpm , then 10,000×g  to remove any remaining insoluble material, the supernatant was pelleted through a 30% sucrose cushion (w/v) at 151,000×g . The pellet was resuspended in PBS (supplemented with 1% [v/v] NP-40 and 0.5% [w/v] sodium deoxycholate), then clarified at 10,000×g  before loading onto a 15–45% (w/v) sucrose gradient for ultracentrifugation at 151,000 × g . 2 ml fractions were collected and those containing VLPs were identified by western blot. A centrifugal filter  was used according to the manufacturer’s instructions to remove sucrose and concentrate the peak fraction to 0.5 mL.Though our aim was to purify VLPs , after glow-discharging the grids in air  using a PELCO easiGlow™ Glow Discharge System (Ted Pella). Following application of sample, grids were incubated at 8 °C, 80% relative humidity for 30 s, then blotted (for 1.0–4.0 s) to remove excess liquid and vitrified in liquid nitrogen-cooled liquid ethane using a LEICA EM GP plunge freezing device . Blotting time parameters were varied to increase the likelihood of recovering a grid with optimal ice thickness. Following plunge freezing, grids were stored in liquid nitrogen. Grids were imaged using an FEI Titan Krios transmission electron microscope  operating at 300 kV. A magnification of 75,000 × was used for a calibrated object sampling of 1.065 Å/pixel. Detailed information on data collection parameters is provided in Supplementary Table 38 was used for image processing. Raw micrographs were first motion corrected using MotionCor239, then CTF parameters were estimated using Gctf40. Particles were selected from a subset of micrographs by manual picking and were then differentiated by 2D classification. High quality 2D classes were used as templates for autopicking the complete data set. A low picking threshold was specified for autopicking to minimize the number of ‘missed’ VLPs. Picked particles were 2 × down-sampled, then subject to 2D classification (with CTFs ignored until the first peak), leading to the identification of FAS-containing classes. 2D classes containing FAS were taken forward for initial model generation and used as templates for FAS-specific autopicking. Autopicked FAS particles were extracted without down-sampling and subjected to two rounds of 2D classification . Particles in high quality classes were taken forward for 3D refinement (with D3 symmetry applied) with subsequent masking and sharpening. Following this, several cycles of CTF refinement (with per-particle astigmatism correction and beamtilt estimation), Bayesian polishing and 3D refinement (with masking and use of solvent-flattened FSCs) were performed to improve the resolution of the map. After sharpening, the resolution of the final map was determined using the ‘gold standard’ Fourier shell correlation criterion (FSC = 0.143)  of 40. Given that an ACP density-containing class was resolved with a “T” number of 40, greater values of “T” number were not tested due to the heavy computational resources required for focussed classification. Particles contributing to the ACP density-containing class were taken forward for asymmetric reconstruction using the relion_reconstruct tool.To resolve the flexible ACP, focussed 3D classification  was performed as described previouslyP. pastoris FAS α (NCBI: XP_002490414.1) and β (NCBI: XP_002489642.1) subunits were used to generate a homology model using the SWISS-MODEL web server29. The homology model was rigid-body fitted into the sharpened FAS density map using UCSF Chimera46, then refined in Coot47. The refined model was symmetrised in UCSF Chimera, then subject to real space refinement in Phenix48 to improve simultaneously the fit of the model to the density map and model geometry. Several iterations between refinement in Coot and refinement in Phenix were performed before validating the model with Molprobity49 and EMRinger50.To generate a preliminary model for the asymmetric unit of FAS, the peptide sequences of 46, UCSF Chimera X51 and PyMOL . Surface charge colouring was performed using PDB2PQR52 and APBS53. Structural alignment and RMSD calculations were performed using the ‘MatchMaker’ tool with default settings in UCSF Chimera. PDBeFold (the protein structure comparison service at EBI [http://www.ebi.ac.uk/msd-srv/ssm])32 was used to probe structural similarity between the FAS structure reported here and other molecular structures within the PDB. Primary sequence alignments were performed using Clustal Omega with default parameters54. Particle orientation distribution was visualized using an adapted version55 of a script from Naydenova & Russo (2017)56.Density map and atomic model visualization was performed and figures were generated using UCSF ChimeraWhere appropriate, statistical details are given in the Method Details section. Supplementary Table Supplementary Information.


The dysfunction of the proteostasis network is a molecular hallmark of neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis. Molecular chaperones are a major component of the proteostasis network and maintain cellular homeostasis by folding client proteins, assisting with intracellular transport, and interfering with protein aggregation or degradation. Heat shock protein 70 kDa (Hsp70) and 90 kDa (Hsp90) are two of the most important chaperones whose functions are dependent on ATP hydrolysis and collaboration with their co-chaperones. Numerous studies implicate Hsp70, Hsp90, and their co-chaperones in neurodegenerative diseases. Targeting the specific protein–protein interactions between chaperones and their particular partner co-chaperones with small molecules provides an opportunity to specifically modulate Hsp70 or Hsp90 function for neurodegenerative diseases. Here, we review the roles of co-chaperones in Hsp70 or Hsp90 chaperone cycles, the impacts of co-chaperones in neurodegenerative diseases, and the development of small molecules modulating chaperone/co-chaperone interactions. We also provide a future perspective of drug development targeting chaperone/co-chaperone interactions for neurodegenerative diseases. Proteins perform different functions that are essential for the physiology of an organism. The proteostasis network maintains the health of the proteome by controlling the protein synthesis, folding, trafficking, disaggregation, and degradation . The dysin vivo acetamide , NVP-HSP990 (38), ONO4140 (39), and Y306zh (40) [In addition, Chan et al. developed a dual luciferase (Renilla and Firefly) reporter system for high-throughput screening and identifying inhibitors of Hsp90–p23 interactions . Using tmide 35, , that in6zh (40) ,134,135.Several Hsp90 co-chaperones contain the TPR domain that mediates the binding to the conserved MEEVD pentapeptide at the CTD of Hsp90 . One of C) and promotes calcium influx through α7 nicotinic acetylcholine receptors (α7nAChRs), which modulates neuronal differentiation and survival [C ligand, Hop also inhibits Aβ oligomers’ binding to PrPC and prevents synaptic loss, neuronal death, and depression of long-term potentiation induced by Aβ oligomers [C and can directly inhibit both Aβ oligomers’ binding to PrPC and neuronal toxicity [C interaction by interfering with the TPR2A domain of Hop and blocks Hop neuroprotective functions [C, leading to a new therapeutic strategy for neurodegenerative disease.Hop is important in HD, AD, and prion diseases . Hop ovesurvival . As the ligomers . The TPRtoxicity . Excess unctions . Therefo41–46  based on the structure of the Hsp90–TPR2A complex . This peY-632 , FKBP 51, and FKBP52. PP5 contains the phosphatase domain in the C-terminal region and three consecutive TPR domains in the N-terminal region for PPIs. Its TPR domains interact with its extreme C-terminal alpha J helix (αJ) in the auto-inhibited state, which blocks substrate access to the catalytic groove . This auin vivo [Drosophila, which expresses Aβ peptides, downregulation of FKBP52 potentiates Aβ toxicity, while FKBP52 overexpression suppresses Aβ toxicity and increases the lifespan [These TPR co-chaperones have important roles in neurodegenerative diseases. PP5 is a serine/threonine protein phosphatase that can dephosphorylate tau at AD-associated abnormal phosphorylation sites and its activity decreases in AD neocortex . PP5 alsin vivo . The funin vivo ,60,61. Oin vivo ,62. Howein vivo . It is sin vivo . In Droslifespan . Thus, flifespan . Moreovelifespan . Therefoarachidonic acid  and two carboxylic acid groups of the C-terminal aspartate or glutamate of the Hsp90/Hsp70 . The resThe development of ligands targeting the dcTPR domain for inhibition of Hsp90/co-chaperone interaction is considered as an alternate strategy for drug development against these targets. However, there are some limitations such as the charged nature of compounds with which to compete and disruption of the dicarboxylate clamp along a poor pharmacokinetic profile of such compounds . AnotherFor most neurodegenerative disorders, only symptomatic treatment is available. There is an urgent need for the development of new therapeutics with disease-modifying properties. Because protein misfolding and aggregation is a major molecular hallmark of these brain disorders, strategies to modulate Hsp70/Hsp90 molecular chaperone networks are particularly attractive. In addition to general difficulties associated with the development of central nervous system (CNS) drugs, life-long treatment will set an additional hurdle for such molecules in terms of their potential toxicity. Therefore, modulation of the Hsp70/Hsp90 network by interference with small molecules affecting interactions between chaperones and their specific co-chaperones can change neuronal proteostasis and ultimately modify the course of the disease.2). This makes it difficult for small molecules to bind with high affinity breaking selective PPIs. Moreover, drugs acting on PPIs tend to have a higher molecular weight (>400 Da) as compared with traditional small molecule drugs (200–500 Da), making it challenging to meet the criteria like Lipinski’s “rule of 5” required for CNS drugs.Modulation of PPIs through small molecules is generally considered difficult for the following reasons. PPIs often occur at the protein surface interfaces of a flat shape and large area (1500–3000 Å2 [With regards to chaperone/co-chaperone interactions, two types of PPIs can be observed: PPIs where the interaction area is large, and PPI inhibitors that would allosterically interact with their targets. For example, only allosteric inhibitors of Hsp70 interactions with J-proteins and NEFs have been developed so far. Such PPI inhibitors would lack selectivity towards particular co-chaperones; however, developing Hsp70 isoform-specific molecules can provide sufficient selectivity for modulation of biological processes. On the other side, PPIs of dcTPR co-chaperones with Hsp70/Hsp90 are mediated by a short C-terminal -EEVD peptide. The -EEVD peptide-binding groove of dcTPR proteins has an area of 650–750 Å2 . Moreove2 . Theoret2 . Most liFinally, modulation of the molecular chaperone network by inhibition of particular chaperone/co-chaperone interactions with small molecules is an attractive strategy to treat neurodegenerative disorders. However, the development of such PPIs as CNS drugs poses great challenges that need to be resolved at the level of drug potency, selectivity, toxicity, and CNS permeability.


Astrocytes play a crucial role in the maintenance of the normal functions of the Central Nervous System (CNS). During the pathogenesis of neurodegenerative diseases, astrocytes undergo morphological and functional remodeling, a process called reactive astrogliosis, in response to the insults to the CNS. One of the key aspects of the reactive astrocytes is the change in the expression and function of connexins. Connexins are channel proteins that highly expressed in astrocytes, forming gap junction channels and hemichannels, allowing diffusional trafficking of small molecules. Alterations of astrocytic connexin expression and function found in neurodegenerative diseases have been shown to affect the disease progression by changing neuronal function and survival. In this review, we will summarize the role of astroglial connexins in neurodegenerative diseases including Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Also, we will discuss why targeting connexins can be a plausible therapeutic strategy to manage these neurodegenerative diseases. Neurodegenerative diseases, presented as the progressive loss of structure or function of neurons, are the main threat to human health, especially for the geriatric population. The most common forms of neurodegenerative diseases include Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS) . It is bFor a long time, neuron-centered theories dominated the research interest of pathogenesis of neurological disorders, whereas the critical role of astrocytes in this process had been over-looked. In the last two decades, the role of astrocytes in the healthy and diseased brain started to gain some recognition. In the adult brain, astrocytes play several crucial roles in supporting neuronal functions, including forming the blood-brain barrier by interacting with endothelial cells, providing nutrients and metabolites support to neurons, and maintaining extracellular ion balance. These functions highly depend on the coordination of hundreds of astrocytes through the formation of an astrocytic network , which i2- and COOH-terminal tails are located in the intracellular space  is a protein family that forms the structural basis of gap junctions. Cx proteins are tetraspanins with two extracellular and one intracellular loop, while the NHar space . Cx monoar space . A clustar space .During reactive gliosis, the expression and function of these Cx proteins changes in astrocytes , 2021, eIn addition, the pannexin (Panx) protein family could also perform Cx-hemichannel-like activity . Panx usThis review will focus on the current understanding of astrocytic Cx in neurodegenerative diseases, including AD, PD, HD, and ALS. We will examine how astroglial Cx, together with Panx, function as hemichannels and contribute toward the development of neurodegenerative diseases. Furthermore, we propose that astroglial hemichannels are potential therapeutic targets for the neurodegenerative diseases.fl/fl), the Cx30 knockout mice (fl/fl:Cx30 KO)  . The expCx30 KO) , and canCx30 KO) . AdditioCx30 KO) . Panx1 wCx30 KO) .The CX43- and CX30-formed GJCs organize astrocytic networks with certain selectivity, which is crucial for normal neuronal function . For exa2+ concentration ([Ca2+]i)  . A recen[Ca2+]i) . Further[Ca2+]i) . The SH3[Ca2+]i) . This in[Ca2+]i) . The sup[Ca2+]i) .in vitro only occurs upon exposure to hypoxia-reoxygenation, pro-inflammatory cytokines, or amyloid-beta (Aβ) treatments -lesioned striatum which leads to neurodegeneration of dopaminergic neurons. In this PD model, the expression of Cx43 and Cx30 in the striatum is increased . A recenn slices . The admn slices , is ablen slices . These dn slices . In Cx30n slices . Howevertrocytes .2+ dynamics, nitric oxide production, gliotransmitter release, mitochondrial morphology, and astrocyte survival   . This uptoxicity , suggestapparent . HoweverThe astrocytic GJCs and hemichannels formed by Cx proteins play important roles in neuroglial interactions. GJCs maintain neuronal homeostasis via astroglial and panglial networks for the trafficking of metabolic substances and elimination of potassium and glutamate. Under pathological conditions, the maintenance of GJC function may be beneficial as it is required for astrocytes to resist oxidative stress . In contAlthough it has been shown that Cx proteins could directly cause neuronal damage via hemichannel function in neurodegenerative diseases, they might also implicate in the disease pathogenesis by alternative mechanisms. Cx43 and Cx30 protein expression is enriched at the astrocyte endfeet at the gliovascular interface, and the absence of these astrocytic Cx proteins weakens the blood-brain barrier function , indicatGiven their role in several neurodegenerative diseases, Cx and Panx hemichannels can be considered as promising alternative therapeutic targets. Hemichannels appear to be more associated with neurotoxicity compared to GJCs  and theiin vitro and in acute hippocampal slices from APP/PS1 mice at the age of 9 months (In neurodegenerative diseases, the development of a potential treatment must consider the needs of long-term treatment and also the use of molecules with the ability to cross the blood-brain-barrier. As such, boldine, an alkaloid compound as mentioned in earlier session, can block Cx43 hemichannels in astrocytes and microglia without affecting GJCs 9 months . Three-m9 months . These r9 months , which c9 months . For exa9 months . Further9 months . However9 months , which lThere is still a need for more in-depth investigations of astroglial Cx proteins, especially Cx43, in the pathology of neurodegenerative diseases not only in AD and PD but also in HD and ALS. Targeting astroglial Cx has become a potential strategy for the intervention or treatment of neurodegenerative diseases. Recent advances in the hemichannel opening mechanism have identified several regulatory regions in Cx43, which could facilitate the drug development targeting Cx hemichannel.XH wrote the first draft of the manuscript. YS and HL revised the manuscript. NW, ZL, and GY edited the manuscript. HC, JN, and CY revised, edited, and supervised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Intrinsically Disordered Proteins (IDPs) lack stable tertiary and secondary structures and are extensively distributed across eukaryotic cells, playing critical roles in cell signaling and regulation ,2,3. IDPThe Special Issue begins with the article by Atieh and colleagues , which dAnother remarkable study within this Special Issue was carried out by Rizzuti and coworkers  on nucleCα, a domain that is C-terminal to the small oligomerization domain (POD) and constitutes the respiratory syncytial virus L-binding region together with POD. By using small phosphoprotein fragments centered on or adjacent to POD, a structural picture of the POD–PCα region in solution was gained, evidencing how small molecules are able to modify the dynamics of PCα. This observed structural plasticity of the PCα domain may play a crucial role for the functional viral polymerase, which needs more investigations.Cardone and co-workers thoroughly investigated phosphoprotein P of Mononegavirales (MNV) , which iThe paper by Ortega-Alarcon and colleagues  investigThe structural features of FOXO3 were analyzed by Weinzierl  through Bokor and Tantos  studied The work presented by Raut and colleagues  investigIn summary, the papers collected in this Special Issue unveil novel aspects related to the wide world of IDPs. Using a variety of methods, including biochemical, spectroscopic and computational techniques, they represent a step forward in the study and characterization of many IDPs involved in human diseases, with a focus on conformational features, environmental effects, recognition mechanisms and targeting.


Qo electron leak; they suppress superoxide generation at complex III of the mitochondrial electron transport chain without inhibiting oxidative phosphorylation. Here we show that feeding different S3QELs to Drosophila on a high‐nutrient diet protects against greater intestinal permeability, greater enterocyte apoptotic cell number, and shorter median lifespan. Hif‐1α knockdown in enterocytes also protects, and blunts any further protection by S3QELs. Feeding S3QELs to mice on a high‐fat diet also protects against the diet‐induced increase in intestinal permeability. Our results demonstrate by inference of S3QEL use that superoxide produced by complex III in enterocytes contributes to diet‐induced intestinal barrier disruption in both flies and mice.The underlying causes of aging remain elusive, but may include decreased intestinal homeostasis followed by disruption of the intestinal barrier, which can be mimicked by nutrient‐rich diets. S3QELs are small‐molecule suppressors of site III Qo of complex III in mitochondria. Treatment of animals on a high‐nutrient diet with S3QELs increases intestinal integrity resulting in an increased lifespan potentially by decreasing ER stress and Hif‐1α signaling.Animals fed a high‐nutrient diet exhibit decreased intestinal integrity over time, which coincides with a decreased lifespan. S3QELs inhibit superoxide production specifically from site III Work from the laboratories of Hansen  used here  cells per intestine  and mitochondria (Sod2), and of catalase (Cat), which dismutates hydrogen peroxide to water and oxygen, in flies fed higher YE%  has the largest capacity of all mitochondrial sites to produce superoxide, which it delivers into both the mitochondrial matrix and the cytosol  or matrix superoxide removal (Sod2) and thereby increase superoxide levels in the two compartments in intestinal enterocytes. Figure Sod1 or Sod2 knockdown, S3QELs protected against the induced intestinal permeability  and matrix (protection against Sod2 knockdown) specifically in enterocytes. Although the Sod knockdown experiments are a good way to manipulate superoxide concentrations independently in the matrix and cytosol to demonstrate their importance, we have not directly measured changes in superoxide concentration in vivo because of the unreliability of the probes that are available . High‐fat feeding has been shown to increase oxidative stress and induce intestinal permeability in mice    Figure  and by t) Figure . In conj) Figure  and muci) Figure . It also) Figure A, increa) Figure B,C and i) Figure .F = 39.795, p = 8.78E−14) and fecal albumin   Figure . Express) Figure . Klf4 ex) Figure . TogetheQo of the mitochondrial electron transport chain in gut epithelial cells, which in turn drives ER stress, increased intestinal permeability, and associated sequelae. This hypothesis explains why treatment with S3QELs protects against ER stress (Figure Weight gain Figure B, and fos Figure  and intes Figure , but nots Figure A.2There is an emerging realization that intestinal barrier dysfunction contributes to almost every major disease, including aging, and it has become critically important to understand the mechanisms that drive this dysfunction (Farhadi et al., Q Electron Leak, and S3QELs, which are specific small‐molecule Suppressors of site IIIQo Electron Leak, offers precise tools to identify and prevent superoxide/hydrogen peroxide production by complex I or III and its downstream effects without interfering with other sites (Brand et al., Q and IIIQo not only have the highest capacity of all mitochondrial sites to produce superoxide/hydrogen peroxide in vitro, but also that they are the main contributors of superoxide in the mitochondrial matrix in several cell lines (Brand, Q and IIIQo in pathologies and physiology (Brand, It is becoming increasingly apparent that the site of superoxide/hydrogen peroxide production is important when understanding and treating pathology given that general antioxidants often have no benefit or have detrimental side effects (Bjelakovic et al., s Brand, . These ty Brand, .Qo (Bell et al., Qo of mitochondrial complex III is a crucial cause of the downstream damage and signaling caused by hyperglycemia and ER stress that leads to pathology.Understanding the mechanisms that drive intestinal barrier dysfunction is crucial to understanding the impact and connection of this barrier to both healthspan and lifespan. Three interrelated candidate mechanisms are ER stress (Gulhane et al., Qo inferred from use of S3QELs contributes to the development of diet‐induced intestinal barrier dysfunction in flies and mice. In flies, raising superoxide levels by Sod knockdown in enterocytes is sufficient to cause intestinal barrier dysfunction. Increased diet‐induced intestinal permeability tightly correlates with decreased lifespan, and feeding S3QELs extends diet‐compromised lifespan in flies. This is important, as our study makes the first robust link from superoxide produced by mitochondrial complex III to the intestinal pathology that impacts lifespan, with important ramifications for mechanisms of metabolic disease and aging. Suppressing superoxide production by site IIIQo in complex III of the mitochondrial electron transport chain using S3QELs has potential therapeutic value in intestinal disorders and premature aging caused by overnutrition.We conclude that superoxide production from site IIIThe authors declare no competing interests.M.A.W., P.K and M.D.B. designed the experiments, M.A.W. performed the experiments and wrote the manuscript; P.K and M.D.B. edited the manuscript. B.P. helped perform succinate feeding and Hif‐1α fly experiments. T.A.U.H. helped with statistical analysis, and J. L‐D. gavaged the mice.Fig S1‐S10Click here for additional data file.


Ultralight bosons such as axion-like particles are viable candidates for dark matter. They can form stable, macroscopic field configurations in the form of topological defects that could concentrate the dark matter density into many distinct, compact spatial regions that are small compared with the Galaxy but much larger than the Earth. Here we report the results of the search for transient signals from the domain walls of axion-like particles by using the global network of optical magnetometers for exotic (GNOME) physics searches. We search the data, consisting of correlated measurements from optical atomic magnetometers located in laboratories all over the world, for patterns of signals propagating through the network consistent with domain walls. The analysis of these data from a continuous month-long operation of GNOME finds no statistically significant signals, thus placing experimental constraints on such dark matter scenarios. A search for transient dark matter in the form of domain walls of axion-like particles finds no statistically significant signal. This places constraints on our theoretical understanding of such scenarios. Although evidence for the existence of dark matter comes from its gravitational interactions, unravelling its nature likely requires observing non-gravitational interactions between dark matter and ordinary matter3. One of the leading hypotheses is that dark matter consists of ultralight bosons such as axions4 or axion-like particles (ALPs)7. Axions and ALPs arise from spontaneous symmetry breaking at an unknown energy scale fSB, which—along with their mass ma—determines many of their physical properties.The nature of dark matter—an invisible substance comprising over 80% of the mass of the Universe10 or composite objects bound together by self-interactions such as boson stars12. Such ALP field configurations could concentrate the dark matter density into many distinct, compact spatial regions that are small compared with the Galaxy but much larger than the Earth. In such scenarios, Earth-bound detectors would only be able to measure signals associated with dark matter interactions on occasions when the Earth passes through such a dark matter object. It turns out that there is a wide range of parameter space—consistent with observations—for which such dark matter objects can have the required size and abundance such that the characteristic time between encounters could be of the order of one year or less12. This opens up the possibility of searches with terrestrial detectors. Here we present the results of such a search for ALP domain walls, a class of topological defects that can form between regions of space with different vacua of an ALP field9. We note that although some models suggest that axion domain walls cannot survive to the present epoch15, there do exist a number of ALP models demonstrating the theoretical possibility that ALP domain walls or composite dark matter objects with similar characteristics17 can survive to modern times20 and have the characteristics of cold dark matter21.ALPs can manifest as stable, macroscopic field configurations in the form of topological defects3, the passage of Earth through an ALP domain wall affects atomic spins similar to a transient magnetic-field pulse12. Considering a linear coupling between the ALP field gradient S, the interaction Hamiltonian can be written asℏ is the reduced Planck’s constant, c is the speed of light, r is the position of spin, t is time, and fSB/ξ ≡ fint is the coupling constant in units of energy described with respect to the symmetry-breaking scale fSB (ref. 22); here ξ is unitless. In most theories, the coupling constants fint describing the interaction between standard model fermions and the ALP field are proportional to fSB; however, fint can differ between electrons, neutrons and protons by model-dependent factors that can be substantial5.Since ALPs can interact with atomic spinsB with atomic spin S can be written asγ is the gyromagnetic ratio. Since equations . The relationships between these parameters and characteristics of the pseudo-magnetic-field pulses searched for in our analysis are discussed in Supplementary Section 22.The amplitude, direction and duration of the pseudo-magnetic-field pulse associated with the transit of the Earth through an ALP domain wall depends on many unknown parameters such as the energy density stored in the ALP field, coupling constant 25; synchronized to the global positioning system (GPS) time; and uploaded to servers located in Mainz, Germany, and Daejeon, South Korea. Descriptions of the operational principles and characteristics of GNOME magnetometers are presented in Methods, Extended Data Table 26.The global network of optical magnetometers for exotic (GNOME) physics searches is a worldwide network searching for correlated signals heralding beyond-the-standard-model physics that currently comprises more than a dozen optical atomic magnetometers, with stations (each with a magnetometer and supporting devices) in Europe, North America, Asia, the Middle East and Australia. A schematic of a domain-wall encounter with GNOME is shown in Fig. 27 despite retaining sensitivity to exotic spin couplings between ALP dark matter and atomic nuclei.The active field sensor at the heart of every GNOME magnetometer is an optically pumped and probed gas of alkali atoms. Magnetic fields are measured by variations in the Larmor spin precession of the optically polarized atoms. The vapour cells containing the alkali atoms are placed inside multilayer magnetic-shielding systems that reduce background magnetic noise by orders of magnitude6–107, as discussed in ref. 28. Therefore, in the present work, we only consider interactions between ALP fields and atomic nuclei. Since all the GNOME magnetometers presently use atoms whose nuclei have a valence proton, the signal amplitudes measured by GNOME due to an ALP-spin interaction are proportional to the relative contribution of proton spin to nuclear spin (as discussed in Supplementary Section 29). This pattern of signal amplitudes  accounts for the specific proton-spin coupling in the respective sensor. This ratio depends on the atomic and nuclear structure in addition to details of the magnetometry scheme  depends on the ALP mass (ma), domain-wall energy density in the Milky Way (ρDW), typical relative domain-wall speed fSB). The region of parameter space to which GNOME is sensitive is defined by the ALP parameters expected to produce signals above the magnitude-to-uncertainty ratio of 12.6 with rates r ≥ R90% during Science Run 2 fSB that fint ≈ 4 × 105 GeV , we expect to considerably improve the sensitivity to ALP domain walls. Additionally, GNOME data can be searched for other signatures of physics beyond the standard model, such as boson stars12, relaxion halos48 and bursts of exotic low-mass fields from black-hole mergers31.Future work of the GNOME collaboration will focus on both upgrades to our experimental apparatus and new data-analysis strategies. One of our key goals is to improve the overall reliability and duration of continuous operation of GNOME magnetometers. The intermittent operation of some magnetometers due to technical difficulties during Science Runs 1–3 made it difficult to search for signals persisting for ≳1 h. Additionally, magnetometers varied in their bandwidths and reliability, as well as stability of their calibration. These challenges were addressed in Science Run 4 through a variety of magnetometer upgrades and instituting daily worldwide test and calibration pulse sequences. However, GNOME suffered disruptions due to the COVID-19 pandemic. We plan to carry out Science Run 5 in 2021 to take full advantage of the improvements. Furthermore, by upgrading to noble-gas-based comagnetometers49 have considered a possible back-action that the Earth may have on a domain wall when certain interactions are important, namely, up-to-quadratic coupling terms between a scalar field and fermions. In contrast to another study49, the present work analyses a completely different interaction, namely, a linear coupling between a pseudo-scalar field and fermion spins, which produces no major back-action effect. Regardless, it would be worthwhile to consider interactions generating similar back-action effects of the Earth on domain walls and the ALP field in later analysis. Further, in future work, we aim to improve the efficiency of the scan over the velocity lattice. The number of points in the velocity lattice to reliably cover a fixed fraction  of the ALP-velocity probability distribution grows as (Δt)–3 . The rolling average is characterized by a 20 s time constant. Noisy frequency bands are suppressed using a first-order Butterworth high-pass filter at 1.67 mHz together with notch filters corresponding to power-line frequencies of 50 or 60 Hz with a quality factor of 60. These filters are applied forward and backward to remove any phase effects. This limits the observable pulse properties to a frequency region to which all the magnetometers are sensitive. Additionally, it guarantees that the duration of the signal is the same for all the sensors. We note that these filter settings may be changed in future analyses.Here we provide more details on the analysis procedure. The identification of events likely to be produced by ALP domain-wall crossings comprise three stages: preprocessing, velocity scanning and post-selectionThe local standard deviation around each point in the magnetometer’s data is determined using an iterative process. Outliers are discarded until the standard deviation of the data in the segment converges. The local standard deviation is calculated taking 100 down-sampled points around each data point.Bj. The uncertainty resulting from the calibration error is later used to determine the agreement with the domain-wall model, but not in the magnitude-to-uncertainty ratio estimate resulting from the model, since the calibration error affects the signal and noise in the same way.Additionally, auxiliary measurements have shown that the calibration factors used by each magnetometer to convert raw data into magnetic-field units experience change over time due to, for example, changes in the environmental conditions. Upper limits on the errors in the calibration factor due to such drifts over the course of Science Run 2 have been evaluated, as listed in Extended Data Table –1 with directions covering the full 4π solid angle is chosen; therefore, the domain walls can take any orientation with respect to the movement of Earth. Note that this distribution considers just the observable perpendicular component of the relative domain-wall velocity and neglects the orbital motion of the Earth around the Sun. For low relative velocities, both time between signals at different magnetometers and signal duration diverge. Therefore, the velocity range is determined by the chosen 97.5% coverage and the maximum relative speed of the domain walls travelling at the Galactic escape speed.Second, at the velocity-scanning stage, data from the individual magnetometers are time-shifted according to different relative velocities between Earth and the ALP domain walls. To sample 97.5% of the velocity probability distribution, a scan of the speeds from 53.7 to 770 km sThe corresponding time-shifted data along with their local standard deviation estimate are fetched from each magnetometer’s rolling-average full-rate data at the rate of 0.1 samples per second. This reduces the amount of data to process, even though keeping the full timing resolution.30 such that, as for the speed scan, a single step in direction results in time-shift differences of less than the down-sampled sampling period. With the settings used, the velocity-scanning lattice consists of 1,661 points. This number scales with the cube of the down-sampled sampling rate.The step size used in the speed scan is chosen so that a single step in speed corresponds to time-shift differences of less than the down-sampled sampling period. For each speed, a lattice of directions covering the full 4π solid angle is constructed. The angular difference between adjacent directions is informed by the sampling rate and speedχ2 minimization is performed for each time point to estimate the domain-wall parameters. An ALP domain-wall-crossing direction and magnitude P-value quantifying the agreement is obtained. The P-value is evaluated as the probability of obtaining the given χ2 value or higher from χ2 minimization. The P-value is calculated using the quadrature sum of the standard deviation of the data and the uncertainty due to drifts in the calibration factors. All the data points in every time-shifted dataset are considered to be potential events, characterized by time, P-value, and direction and magnitude ζ is the ratio between After the time shift, the pulses produced by a domain-wall crossing simultaneously appear as if all the magnetometers were placed at the Earth’s centre. This process results in a time-shifted dataset for each lattice velocity on which P-value. The directional-consistency test is based on the angular difference between the estimated domain-wall-crossing direction and the direction of velocity corresponding to the particular time-shifted dataset being analysed. In a real domain-wall-crossing event, these two directions should be aligned.Third, in the post-selection stage, two tests are carried out to check if a potential event is consistent with an ALP domain-wall crossing. The domain-wall model test evaluates if the observed signal amplitudes are consistent with the expected pattern of a domain-wall crossing from any possible direction. It is quantified by the aforementioned P-value and the angular difference normalized with respect to the angular spacing of the lattice of velocity points for that speed. The thresholds are chosen to guarantee a detection probability of 97.5% with the minimum possible false-positive probability. The false-positive analysis is performed on the background data. The true-positive analysis is performed on the test data consisting of background data with randomly inserted domain-wall signals as described below.To evaluate the consistency of a potential event with a domain-wall crossing, we impose thresholds on the A single signal pattern may appear as multiple potential events in the analysis, whereas we are seeking to characterize a single underlying domain-wall-crossing event. For example, a signal consistent with a domain-wall crossing lasting for multiple sampling periods would appear as multiple potential events in a single time-shifted dataset. Furthermore, even if such a signal lasts only for a single sampling period, the corresponding potential events appear in different time-shifted datasets. Since it is assumed that domain-wall crossings rarely occur, such clusters of potential events are classified as a single ‘event’. To reduce the double counting of these events, conditions are imposed. If potential events passing the thresholds occur at the same time in different time-shifted datasets or are contiguous in time, the potential event with the greatest magnitude-to-uncertainty ratio is classified as the corresponding single event.53. A set of pseudo-magnetic-field pulses matching the expected amplitude and timing pattern produced by the passages of Earth through the ALP domain walls are inserted into the background data to create the test data.To evaluate the detection probability of the search algorithm, a well-characterized dataset that includes domain-wall-crossing signals with known properties is required. For this purpose, we generate a background dataset by randomly time shuffling the search data so that the relative timing of measurements from different GNOME stations is shifted by amounts so large that no true-positive events could occur. By repeating the process of time shuffling, the length of background data can be made to far exceed the search data. This method is used to generate background data with noise characteristics closely reproducing those of the search data4 pT and durations randomly selected between 0.01 and 103 s. The distributions of the these randomized parameters are chosen to be flat on a logarithmic scale. Additionally, the signals are inserted at random times with random directions. To simulate the effects of calibration error, the pulse amplitudes inserted in each magnetometer are weighted by a random factor whose range is given in Extended Data Table P-value, normalized angular difference and magnitude-to-uncertainty ratio are computed.The true-positive analysis studies the detection probability as a function of the thresholds. Multiple test datasets are created featuring domain-wall-signal patterns with random parameters by inserting Lorentzian-shaped pulses into the background data of the different GNOME magnetometers. The domain-crossing events have magnitudes of mentclass2pt{minimP-value and the threshold on the upper limit of the normalized angular difference. We restrict the analysis in Extended Data Fig. ζ = 5 is below 0.01% in a 10 s sampling interval. Since the detection probability increases with the signal magnitude, we focus on the events below ζ = 10. The detection probability is then the number of detected events divided by the number of inserted events. The black line marks the numerically evaluated boundary of the area, guaranteeing at least 97.5% detection. All points along this black line yield the desired detection probability; therefore, this particular choice is made to minimize the number of candidate events when applying the search algorithm to the background data. Here the values determined for the P-value threshold and directional-consistency threshold are 0.001 and 3.5, respectively  is the number of candidate events found in the background data above ζ. The significance is then defined as –1 is the inverse error function. The significance is given in units of the Gaussian standard deviation that corresponds to a one-sided probability of P.Since the noise has a non-zero probability of mimicking the signal pattern expected from an ALP domain-wall crossing well enough to pass the ζ > 6, only a few events were found. The event with the largest magnitude-to-uncertainty ratio, ζmax, was measured at 12.6 followed by additional events at 6.2 and 5.6. From equation . Such prepocessing effects rely on a Lorentzian-shaped signal and give rise to the characteristic shape shown in Fig. T is defined as the amount of time for which the network can measure a domain wall with duration Δt and magnitude ζ ≥ 1. Monte Carlo simulations analysing segments with inserted domain-wall encounters on the raw data show good agreement with the sensitivity estimation in equation :v⊥, the signal duration isThe domain-wall thickness is determined by the ALP mass, and is of the order of the ALP-reduced Compton wavelength ρDW ≈ 0.4 GeV cm–3 in the Milky Way24, the energy per unit area (surface tension) in a domain wall, σDW, determines the average separation between the domain walls, L. The surface tension σDW is related to the symmetry-breaking scale9 asWe assume that domain walls comprise the dominant component of dark matter. Thus, with the energy density r ≥ R90%. The experimental constraint on the coupling constant is written as follows . In contrast to the signal duration, the pseudo-magnetic-field signal depends on all the parameters of the ALPs, mass, and ratio between the coupling and symmetry-breaking constants, namely, ζ = 12.6. The shape of the constrained space is given by the fact that T varies depending on the target ma and ξ.The ALP parameter space is constrained by imposing equation  in Supplequation .13\documAny methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41567-021-01393-y.Supplementary InformationSupplementary text.


To design a reliable forming process it is necessary to determine the mechanical and formability properties of the processed material, which are used as input parameters for forming simulations. High-strength steel is irreplaceable as a material for producing the deformation zones of current automobiles. This type of steel can be processed by conventional or unconventional forming methods. In the sheet forming process, the material is usually under uniaxial and biaxial stress. The bulge test is utilized for determination of biaxial stress–strain curves, which are often used as input material data for forming simulations. In this work, numerical simulations of bulge tests using TRIP RAK 40/70 steel were performed to study the impact of yield criteria and hardening laws on the accuracy of thickness prediction of the deformed steel sheet. Additionally, the impact of different solvers and integration schemes on the thickness prediction was tested. Furthermore, the impact of various degrees of deformation (various dome heights) on thickness prediction accuracy was evaluated. Numerical results showed a good correlation with experimental data. When the Hill90 yield criterion was used, the software with implicit solver was more accurate in predicting thickness compared to software with explicit integration scheme, in most cases. In addition, the thickness prediction of parts with lower deformation was more accurate compared to parts with greater deformation (higher dome height). It is for the above-mentioned reasons that steel sheets with higher strength properties, which are characterized by good plasticity, have been widely used in recent years in the production of car bodies [The automotive industry is currently one of the fastest growing industries in the world. Demands to reduce vehicle weight have led to the development and application of high-strength steels in the last three decades. Car body weight reduction requirements reduce fuel consumption and thus reduce COr bodies ,4,5,6.One such steel that has been developed and used in recent years in the production of car bodies is the plastic-induced steel “TRIP”. These steels have higher strength and have sufficient plasticity in cold forming. During cold plastic deformation of these materials, austenite transforms into deformation-induced martensite. This contributes to the overall strengthening of the pressed sheet ,5,7. SheThe uniaxial tension test is used to measure mechanical and elasto-plastic properties of materials, but in real forming processes the biaxial stress state is also present. Thus, it is important to test materials in this type of stress state to accurately predict their deformation behavior in real stamping applications ,10. The The research work of Slota et al.  was aimeThe mechanical and plastic properties of the materials are necessary input parameters for the finite element analysis (FEA) of forming processes. FEA is an established tool for the calculation and analysis of sheet deformation ,14,15,16Based on current knowledge, there are four categories of parameter that are vital for accurate prediction in numerical simulation ,24,25, nThe presented work deals with the comparison of bulge test simulation results of RAK 40/70 TRIP steel obtained using two software programs that are often used for prediction and analysis of forming processes. The impact of different yield surface models and hardening models on the simulation accuracy was also studied. The predicted parameters obtained by the numerical simulation and the measured results were compared. Optical and conventional methods were used to measure the thickness of samples. The change in the thickness of the material of the resulting bulge at different degrees of deformation was compared. The novelty of the presented work is the comprehensive analysis of the effect of input material, which combines numerical thickness prediction and the bulge test results using specimens made of TRIP steel under various degrees of deformation.In the experiments for this work, a 0.75 mm thick steel sheet made of TRIP steel RAK 40/70 Z100MBO  was used. The sheet was hot-dip galvanized. TRIP steels have been used for experimental research because their structure differs significantly from that of low-carbon and low-alloy steels. The structure of these steels consists of ferrite, bainite, martensite and residual austenite. The plastic deformation, therefore, takes place on other principles compared to mild steels. Residual austenite is transformed into martensite in deformation and is found in a polygonal ferrite matrix including bainite, which retains very good ductility .The material’s microstructure affects the process of plastic deformation and the material’s strengthening during plastic deformation. We assume a different mechanism of hardening and plastic deformation than in low-carbon and low-alloy steels. Different materials, therefore, behave differently and have certain specific properties in elastic and plastic deformation ,31,32,33In order to obtain objective results from Finite Element (FE) simulation of the forming processes, it is necessary to enter as inputs the characteristic properties for a specific type of steel sheet . TherefoThe measured properties of the material determined by the tensile test are shown in As mentioned above, to obtain the properties of the examined sheet, average values given in The average values and planar anisotropy of the plastic strain ratio and the strain-hardening exponent were calculated with the use of the following equations:To compare the properties of the tested material under different stress and strain schemes, a bulge test was used in the experiment. The test was performed on equipment of our design. This device is shown in 0 = 80 mm). The test specimens were deformed gradually to a height corresponding to the deformation for determining the strain-hardening exponent by a uniaxial tensile test (5–15%). The samples were labeled sequentially from T8 to T3. Samples with T8 designation were deformed up to 5.2 mm bulge height, samples T7 were deformed up to 9.9 mm, samples T6 were deformed up to 13.5 mm, samples T5 were deformed up to 14.4 mm, samples T4 were deformed up to 18.8 mm and samples T3 were deformed up to 19.3 mm.The advantage of the designed equipment for the bulge test is the fact that a test specimen is used for the test—sheet metal with dimensions of 130 mm × 130 mm. The average values from this test were determined using 6 test samples for 6 various degrees of deformation (bulge heights). The diameter of the die in which the hemispherical dome is formed is 80 mm. This diameter was chosen because it corresponds to the length of the measured part of the test specimen at uniaxial tension , it is needed to take images of the sample in various positions and angles. Then, all the acquired high-resolution images and 2D coordinates of all the dots are mathematically derived and recalculated to 3D coordinates using photogrammetry principles, taking into account ray intersections, camera positions, and lens distortion. The main result is a fine 3D point cloud, consisting of a large number of points, which represent the 3D surface of the sheet metal part. By evaluating the relative distance between points, the stress, strain and thickness of the deformed sample can be calculated and evaluated.During bulge testing, the points of the deformation grid on the sample in the deformation areas shifted. The diameter of one grid point was 1 mm and the distance between points was 2 mm. The images of the samples were taken using a digital camera Schneider KREUZNACH CINEGON 1.4/12 . Taken images were processed using GOM Inspect software  and the thickness of the bulge test samples was evaluated .The plastic deformation process in the bulge test was simulated using two FEM forming programs that are often used by the automotive sector. The same bulge test input data were used to compare the two selected software systems. Both types of software are based on the finite element method. Two types of software were used to compare the simulation results – one with the implicit solver and triangular elements and the other with the explicit solver and hexagonal elements. In both cases, the deformation process model of the bulge test was created so that the specimen flange was firmly fixed, to prevent the test specimen material from sliding under the flange area. The plastic deformation took place at the expense of the change in the thickness of the material in the die with 80 mm diameter. The accuracy of the numerical simulation was set to fine. The default size of the blank shell element was user-defined and set to 3 mm, and maximum refinement level of the element was set to 3 . Radius penetration was set to 0.16 and the number of integration points in the sheet metal thickness was set to 5. The plastic deformation was carried out using the pressure of the hydraulic fluid supplied under the test specimen. In both types of software, the input data on the hardening curves (according to Hollomon and Krupkowski) were used and the yield criteria according to the Hill48 and Hill90 models were set. The numerical simulation aimed to analyze the differences in the predicted thickness of deformed specimens caused by the change of the input hardening laws and plasticity models, which in the simulation represent the tested material - TRIP steel.The transition of material from elastic deformation to plastic is most often defined by the yield strength. The yield strength may vary with different stress patterns. The different values will be for uniaxial and biaxial tensile stress of the sheet . From thTwo yield models were examined in this research. A Hill48 yield surface model and Hill90 yield criteria were used to evaluate the influence of the yield criterion on the simulation accuracy . The aniThe obtained simulation results were analyzed in comparison with the sample thickness values measured with the optical measuring system (ARGUS) and the thickness values measured manually using the digital micrometer.The hardening law is necessary to completely define material behavior during deformation. The hardening of the material during cold plastic deformation depends mainly on the structure and microstructure of the formed material. There exist three main types of rules describing material hardening . IsotropHollomonσ represents the true stress, K is the strength coefficient, n is the strain-hardening exponent, φ0 is the pre-strain and plφ represents the plastic strain. Material model constants used in both hardening rules can be seen in KrupkowskiIn the presented work, two isotropic hardening laws were used in numerical simulations. Isotropic hardening rules are defined as:The results of the hydraulic bulge test, in which the stress–strain behavior was determined at individual degrees of plastic deformation (different bulge heights) are shown in The thickness results of the specimen are presented in The FE analysis aimed to analyze the thickness differences caused by changing hardening models and yield criteria in simulation. Additionally, the impact of the integration scheme (implicit/explicit) was evaluated. The comparison of predicted thickness values of samples T3 and T6 using software with explicit solver are presented in The predicted thickness of the formed part showed the best correlation with measured thickness values when the Hill90 yield surface model combined with the Hollomon hardening model was used in simulation software with the explicit solver. The combination of the Hill90 yield surface model and Krupkowski hardening model showed the best correlation of thickness results when simulation software using implicit solver was used. Overall, smaller deviations of predicted thickness were observed for samples with lower deformation, lower dome height  and higher deviations of thickness were observed in samples with greater deformation, higher dome height . The results obtained using software with implicit solver showed a better correlation with experimental thickness results than results obtained using the software with explicit solver.t from the measured thickness values are shown in Dev X is a mean deviation of predicted thickness value from the experimental thickness value, simX is the simulation thickness value and expX is the experimental thickness value.Mean deviations of predicted thickness Previously published research works regarding forming prediction of bulge test specimens focused predominantly on the analysis of material parameters and their effect on the forming prediction accuracy in numerical simulations ,37,38,39Furthermore, the yield criteria influence on the thickness accuracy prediction was evaluated. Hill48 and Hill90 yield criteria were combined with isotropic hardening laws. The comparison of yield surface  shows thThe degree of deformation of a bulge test specimen, and its impact on the numerical prediction of thickness accuracy was studied. Based on the numerical results, it can be stated that the accuracy of thickness prediction for samples with greater deformation (sample T3, Hollomon’s hardening law exhibited better accuracy in predicting thickness in comparison with the Krupkowski hardening law in most cases.The Hill90 yield criterion was found to be more suitable for simulation of bulge test using TRIP steel.Software with implicit solver and triangular shell elements showed better accuracy in predicting thickness of deformed sheet in bulge test than software with explicit solver and hexagonal shell elements in most cases.The degree of deformation impacted the simulation accuracy, while thickness prediction of samples with greater deformation was less accurate in comparison with samples with lower deformation and lower bulge height.The forming prediction accuracy of high-strength steel can pose a challenge in numerical simulation. This research investigated the influence of yield criteria and hardening laws on the accuracy of thickness prediction of bulge test specimens made of TRIP RAK 40/70 steel with the use of the FE method. Additionally, the impact of solver type and degree of deformation on the accuracy of numerical simulation was studied. The results lead to the following findings: