The NLRP3 inflammasome is an intracellular multiprotein complex that plays an essential role in the innate immune system by identifying and eliminating a plethora of endogenous and exogenous threats to the host. Upon activation of the NLRP3 complex, pro-inflammatory cytokines are processed and released. Furthermore, activation of the NLRP3 inflammasome complex can induce pyroptotic cell death, thereby propagating the inflammatory response. The aberrant activity and detrimental effects of NLRP3 inflammasome activation have been associated with cardiovascular, neurodegenerative, metabolic, and inflammatory diseases. Therefore, clinical strategies targeting the inhibition of the self-propelled NLRP3 inflammasome activation are required. The transcription factor Nrf2 regulates cellular stress response, controlling the redox equilibrium, metabolic programming, and inflammation. The Nrf2 pathway participates in anti-oxidative, cytoprotective, and anti-inflammatory activities. This prominent regulator, through pharmacologic activation, could provide a therapeutic strategy for the diseases to the etiology and pathogenesis of which NLRP3 inflammasome contributes. In this review, current knowledge on NLRP3 inflammasome activation and Nrf2 pathways is presented; the relationship between NLRP3 inflammasome signaling and Nrf2 pathway, as well as the pre/clinical use of Nrf2 activators against NLRP3 inflammasome activation in disorders of the central nervous system, are thoroughly described. Cumulative evidence points out therapeutic use of Nrf2 activators against NLRP3 inflammasome activation or diseases that NLRP3 inflammasome contributes to would be advantageous to prevent inflammatory conditions; however, the side effects of these molecules should be kept in mind before applying them to clinical practice. Neuroinflammation, a term used to define a wide variety of innate and adaptive immune responses within the brain and the spinal cord, contributes greatly to the pathogenesis of acute and chronic central nervous system (CNS) disorders . In the via PYR-PYR interaction.NLRP3 inflammasome is an essential component of the innate immune system, which provides defense against infections caused by bacteria, fungi, and viruses , 8. The via its PYD domain. After that, the NLRP3-ASC complex recruits pro-caspase-1 via the CARD domain. The activated caspase-1 cleaves pro-IL-1β and pro-IL-18  regulates NLRP3inflammasome g domain . Aryl hyg domain , B-cell g domain , cAMP-PKg domain , AMPK-GSg domain , and Revg domain  inhibit g domain , TLR sigg domain  and TLR4g domain , mTOR sig domain , MAPK sig domain , and NF-g domain  activateg domain , miR-22 g domain , miR-30eg domain , miR-133g domain , miR-223g domain  were foug domain , Meg3  , ParkinsAD is the most common progressive neurodegenerative disorder of the CNS, characterized by abnormal accumulation of β-amyloid plaques in the extracellular space and hyperphosphorylation of tau protein in cytoplasmic neurofibrillary tangles. Previous studies have demonstrated that both hyperphosphorylation of tau  and aggrvia distinct mechanisms like obstruction of K+ efflux , hampering oligomerization, or binding of ASC , and preventing auto-cleavage of pro-caspase-1 (FC11A-2)  . They alFC11A-2) .ClinicalFC11A-2) , 70. Altvia blocking the interaction between nuclear exportin and nuclear export sequences. Heterodimerization of Nrf2 with Maf protein promotes nuclear retention of Nrf2. The heterodimer of Nrf2-Maf plays a master regulatory role against stress conditions by activating antioxidant-responsive element (ARE) or electrophile-responsive element (EpRE) containing genes. As one of the essential tasks of Nrf2 is to provide the necessary anti-oxidative response, it can be considered as a pioneer cell survival tool. Therefore, Nrf2 can be found dysregulated or disrupted in many diseases such as metabolic diseases, neurodegenerative diseases, aging, inflammatory diseases, cancer, and cardiovascular diseases  is a basic leucine zipper (bZIP) that belongs to the Cap ‘N’ Collar (CNC) family, and with its cytoplasmic repressor Kelch-like ECH-associated protein 1 (KEAP1), are the master regulators of redox homeostasis in the cells . Under ndiseases .Nrf2 has seven Nrf2-ECH homology regions, Neh1–7, each performing different functions. Neh1 contains the CNC-bZIP region required for DNA binding and association with small musculoaponeurotic fibrosarcoma (sMaf) proteins, which are partners of Nrf2 dimerization . Neh2 coKEAP1 is a substrate adaptor molecule for Cul3-containing E3 ubiquitin ligase which interacts with Nrf2 and downregulates Nrf2. KEAP1 has five domains, namely N-terminal region (NTR), Broad complex Tramtrack and Bric-à-Brac (BTB) domain, intervening region (IVR), Kelch domain/double glycine repeat (DGR), and C-terminal region (CTR) . The DGRvia modification of protein stability and binding partners. KEAP1 protein is responsible for mediating the first and most important mode of regulation of Nrf2 activation. Under homeostatic conditions, two KEAP1 molecules bind to Nrf2 via the ETGE and DLG motifs to the Neh2 domain   and NF-κNrf2 gene disrupts the KEAP1/Nrf2 interaction, thereby leading to enhanced Nrf2 activity  inhibitors, and multi-target drugs. Electrophiles are electrophilic molecules that alter cysteine residues of the KEAP1 protein by certain modifications like oxidation. Some electrophiles are triterpenoids like bardoxolone-methyl and RTA-408 , fumaricTripterygium wilfordi root extract, exhibited neuroprotective properties in a MPTP-induced PD mouse model and AAV-mediated human α-synuclein overexpression in a PD model by inhibiting NLRP3 through the Nrf2 signaling pathway  model rats  model BV2 microglial cells  by both Food and Drug Administration and the European Medicines Agency , and resveratrol derived from nuts and berries are the most clinically tested compounds after SFN. Because of their anti-oxidative and anti-inflammatory properties, as well as their ability to react with KEAP1 via its cysteine 151 residue, they could serve as potent Nrf2 modulators  and clinical studies indicates that the contribution of NLRP3 inflammasome activation to a wide variety of diseases remains largely unelucidated. Furthermore, the ablation of NLRP3 inflammasome-related genes or small inhibitors against the NLRP3 inflammasome complex alleviates the progress, severity, or clinical outcomes of NLRP3-associated diseases. Therefore, the need for developing therapeutic strategies targeting the structural integrity or activity of the NLRP3 inflammasome complex is indispensable. Although developing novel drugs or repurposing currently used drugs might be effective in terms of specificity, the pharmacological modulation of cytoprotective signaling pathways might be another therapeutic approach to suppress the NLRP3 inflammasome activation and subsequent cell death. Since NLRP3 inflammasome senses ROS, inflammatory cytokines, and endogenous metabolites, which damage homeostasis as a regulator of redox, metabolism, and inflammation, Nrf2 represents a powerful candidate for targeting the NLRP3 inflammasome signaling cascade.Since the discovery and molecular/biochemical elucidation of the NLRP3 inflammasome by Tschopp and his colleagues in 2002, cumulative evidence acquired by preclinical (Given that Nrf2 regulates more than 250 homeostatic genes, Nrf2 plays a pivotal role in cytoprotection. This role is also supported by the presence of a plethora of naturally derived and designed Nrf2 inducers, which modulate Nrf2 and enhance its cytoprotective effects. Furthermore, some Nrf2 inducers, such as DMF and SFN, have undergone trials in preclinical and clinical models of diseases with which NLRP3 inflammasome activation is associated. As the recent advances and studies enhance our knowledge on the NLRP3/Nrf2 crosstalk, the need to establish specific therapeutic approaches with precision using Nrf2 inducers increases. To achieve this, more comprehensive and multidisciplinary research efforts are needed. Nevertheless, this research will be translated and broaden our current insight into the regulation of NLRP3 inflammasome and its related diseases.BT and BA wrote the first draft of the manuscript. SG revised and approved the submitted version of 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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G-quadruplexes constitute an important type of nucleic acid structure, which can be found in living cells and applied by cell machinery as pivotal regulatory elements. Importantly, robust development of SELEX technology and modern, nucleic acid-based therapeutic strategies targeted towards various molecules have also revealed a large group of potent aptamers whose structures are grounded in G-quadruplexes. In this review, we analyze further extension of tetraplexes by additional structural elements and investigate whether G-quadruplex junctions with duplex, hairpin, triplex, or second G-quadruplex motifs are favorable for aptamers stability and biological activity. Furthermore, we indicate the specific and pivotal role of the G-quadruplex domain and the additional structural elements in interactions with target molecules. Finally, we consider the potency of G-quadruplex junctions in future applications and indicate the emerging research area that is still waiting for development to obtain highly specific and effective nucleic acid-based molecular tools. Aptamers are single-stranded oligonucleotides with specified sequences which determine the adoption of a strictly defined spatial structure. This enables highly specific binding to target molecules and modulation of their activity . The oriA significant proportion of aptamers constitute G-quadruplexes presumably due to a greater negative charge density, which is twice as for double-stranded DNA, and which could influence the enhancement of interactions with proteins . What isHerein, structural engineering of G-rich aptamers by the junction with various structural motifs will be discussed. This interesting approach could provide improvements in aptamers affinity towards target compounds, multivalency of binding , or chanThe mutual connection of G-quadruplex and the double helix is one of the most commonly described types of tetraplex junction. G-quadruplexes are characterized by various topologies depending on the relative orientation of strands within the core and the types of spanning loops. Importantly, the types of groves, i.e., wide, medium, or narrow, within the G-quadruplex core, depend on its folding topology . In contThe junction of antiparallel DNA G-quadruplex with the duplex domain aligned across the wide groove results in a gradual transition between the two structural elements with important stabilizing input of coaxial stacking of heterocyclic residues over the duplex and G-quadruplex motifs A. IntereThe strategic positioning of both structural elements within quadruplex–duplex junctions was adopted to improve the properties of many G-quadruplex-based aptamers with a particular emphasis on anticoagulants.The most well-known G-quadruplex-based aptamer is thrombin binding aptamer . It was discovered in 1992 by L. Bock as a consequence of the canonical SELEX method . TBA is One of the pioneering works in the field of quadruplex–duplex aptamers was described in 1995 by Macaya et al. . The autThe topology of the G-quadruplex motif in the junctions was the same as for the isolated G-quadruplex molecule. Interestingly, the additional duplex domain was formed by 5′ATGT and 5′GTAG more often than by other sequences. Nevertheless, it was difficult to unambiguously indicate the optimal duplex motif sequence, which could be responsible for improved affinity to thrombin. NMR studies indicated that the duplex domain contributes to the overall stability of the G-quadruplex motif. Moreover, the presence of the helix might be partly responsible for the higher affinity of the aptamers to thrombin. However, no strict correlation between stability and affinity was found. The newly selected quadruplex–duplex aptamers competed with their G-quadruplex counterparts for binding with thrombin, indicating that the anion-binding exosite is the domain that interacts with the new aptamers. Importantly, the studies showed that increased affinity to thrombin of quadruplex–duplex junctions is not the result of non-specific binding but rather is a consequence of additional or improved contacts with thrombin. However, the binding affinity was not strictly correlated with anticoagulant properties of selected quadruplex–duplex aptamers and some of the molecules appeared to be poor inhibitors of fibrin clot formation regardless of high binding affinity. Nevertheless, spanning both ends of the aptamers with triethylene glycol or a disulfide bond resulted in significant improvements of their enzymatic resistance and anticoagulant properties.An alternative approach to the SELEX method, using the evolution-mimicking algorithm (EMA), was proposed by the Karube group . The iniSimilar constructs were described by Tasset et al. ; HD22-29In 2011 Mazurov et al. characterized a new antithrombin aptamer named RE31 . The molThe attempts of Kotkowiak et al. to characterize chemically modified variants of RE31 indicate no strict correlation between aptamers thermal stability and inhibitory activity of fibrin clotting . The aptCurrently, the only quadruplex–duplex junction which has entered Phase II clinical trials is NU172, also known as ARC2172 , NU172. Interestingly, the comparative structural studies of four quadruplex–duplex junctions, i.e., RE31, HD22 (27 mer and 29 mer), and NU172 revealed that such constructs change conformation depending on the type of monovalent cations with the exception of RE31 . FurtherDespite a large number of articles concerning anticoagulant quadruplex–duplex junctions, some G-quadruplexes structural functionalization with a helix domain has been described to obtain highly selective aptamers towards targets other than thrombin. Recently, a characteristics of novel quadruplex–duplex junction based on AS1411 aptamer was published  G-quadruplex fragment . Th. Th81]. Almost simultaneously, Ikebukuro and co-workers developed a compound also composed of HD1 and HD22(29) aptamers but linked by poly-dT linkers with lengths ranging from 5 nt to 20 nt   in thrombin inhibition was also noticed by Kim et al. . The groFurther continuation of the research trend concerning complexation of HD1 with HD22(29) was performed by the Veedu group, who developed conjugates composed of the above aptamers linked by triethylene glycol , RNV220 m for variants was in range from 33 to 35 °C, Tm for TBA was equal to 33 °C) and slightly better than for unmodified RA36 (Tm was 32 °C). The two opposite exceptions constituted variants possessing linkage composed of two adenosine residues (AA) and thymidine residues with a Gly moiety (TGlyT). The quadruplex–quadruplex junction mediated by AA was characterized by a melting temperature (37 °C) that was higher than for others, whereas the one linked by TGlyT showed the lowest thermal stability . Moreover, the variant which contained AA also possessed the most favorable inhibitory properties and the highest biostability. The improvement in thrombin inhibition, in comparison to wild-type TBA, was also observed for junctions with TT and AGlyA linkers. Based on CD spectra analysis, the authors concluded that G-quadruplex motifs within all analyzed variants preserved an antiparallel G-quadruplex structure. Importantly, it was also claimed that the type of linker had an influence on the biostability and that the variants containing a glycerol moiety were more prone to enzymatic degradation. Another interesting concept in the exploration of the potency of G-quadruplex complexation was the construction of a homodimer, termed RA36, composed of two HD1 motifs covalently linked by one thymidine residue B 63]. O. O63]. OAn innovative approach was proposed by Di Giusto and King, who performed a complexation of two or more G-quadruplexes via circularization  65]. Th. Th65]. C) and preventing its transition into an abnormal form (PrPSc). The R12-based G-quadruplex junctions were composed of two R12 aptamer molecules , they were characterized by superior affinity toward thrombin . Interestingly, all tested molecules displayed a similar activity level in functional activity tests (thrombin time test), which was slightly lower than for HD1. Noteworthy, the comparison of anticoagulant properties of HD1-ds with RA-36 revealed the differences in their mode of action. RA-36 was characterized by a lower inhibitory activity than HD1-ds, therefore, it could be concluded that HD1 motifs in this conjugate act as one unit and that the presence of covalent linkage causes a decrease in anticoagulant properties. On the contrary, only a small variation in thrombin inhibition in comparison to HD1 was observed for HD1-ds. Thus, it could be assumed that its components functioned as two independent domains and their activity depends only on the accessibility of exosite I. The CD analysis indicated that both conjugates exhibited features of antiparallel G-quadruplexes. Additionally, the spectrum of HD1-ds possessed also a minimum at 245 nm, which is typical for DNA duplexes.Apart from the covalent junction, the bivalency in aptamers constructs could also be obtained by non-covalent assembly ,84. In tM for HD1 and HD22 with an additional single-stranded part was 40.5 °C and 44.3 °C, respectively vs. TM for FF and FH was 38.1 °C and 40.1 °C, respectively), simultaneously with a slightly stabilizing effect on the duplex fragment . Interestingly, despite lower thermal stability, both dimeric aptamers were characterized by a significant improvement in anticoagulant properties in comparison to the parental HD1. In addition, the inhibitory activity was strongly dependent on the presence of potassium ions. It is worth noting that the above system was also used for the development of an aptasensor platform dedicated to thrombin detection, where dimeric aptamers were immobilized on a gold surface covered by neutravidin . Al. Al68]. The second example of a G-quadruplex-hairpin junction constitutes the aptamer targeted towards hemin, being a DNAzyme with peroxidase-like activity . The conc-Myc oncogene and a hairpin fragment localized at its 3′-termini  which interacts via Hoogsteen  or reverse-Hoogsteen  hydrogen bonds ,86. An interesting example of quadruplex–triplex junction is the codeine-binding aptamer , CBA-1 . This olG-quadruplex-based aptamers constitute a large and promising group of nucleic acid-based molecular tools with high therapeutic potential. Interestingly, the biological activity of molecular tools based on G-quadruplexes can be successfully improved by adding additional structural elements. To date, the majority of structural modifications concerns quadruplex–duplex and quadruplex–quadruplex junctions . Based oAnother very interesting group of junctions studied to date is the quadruplex–quadruplex connection. In this approach, two strategies can be recognized, i.e., the junction of two different G-quadruplex domains or the connection of two G-quadruplex motifs of the same type. In the case of the first option, both domains are usually targeted towards different active sites of the same molecule increasing the bivalency of the junction. This solution results in multiple binding events and improved target selectivity. On the contrary, the junction of two identical G-quadruplexes leads to novel aptamer targeted towards the same binding site. In such a situation, one domain is responsible for the inhibitory potential of the junction, whereas the second one controls the action of the first one. Furthermore, the composition and length of the linker, which joins both G-quadruplex motifs, is essential for the activity of the quadruplex–quadruplex junctions. Importantly, the superior biological activity of the junctions can be successfully achieved by the application of covalently bound linkers as well as of the non-covalent junction of two G-quadruplex domains.A few articles describing other than a duplex or G-quadruplex connection with G-quadruplexes indicate that additional structural elements such as a triplex or hairpin clearly influence the thermal stability and biological activity of the G-quadruplex domain. Moreover, similar to other structural motifs, also here the composition and/or length of an additional domain plays an important role in the folding and inhibitory activity of the whole junction.The promising physicochemical and biological characteristics of G-quadruplex junctions with other structural motifs studied so far provide preliminary evidence for the favorable modification of G-rich structures. Undoubtedly, the connection of G-quadruplexes with helices, hairpins, triplexes, or other G-quadruplexes appear favorable from the therapeutic point of view, thus increasing the repertoire of molecules potentially useful in the therapy of human diseases. The doors for the development of new molecular tools based on nucleic acids have been already opened. However, the area of research discussed herein still requires detailed studies on the requirements needed to design G-quadruplex junctions with superior properties. Importantly, the combination of structural and chemical modifications of G-quadruplex-based junctions and their properties are still mostly a blank slate, thus leaving a great scope of activity for future investigations.


Latrodectus spiders. The venom contains a toxic cocktail of seven LaTXs, with one of them targeting vertebrates (α-latrotoxin (α-LTX)), five specialized on insects , and one on crustaceans (α-latrocrustatoxin (α-LCT)). LaTXs bind to specific receptors on the surface of neuronal cells, inducing the release of neurotransmitters either by directly stimulating exocytosis or by forming Ca2+-conductive tetrameric pores in the membrane. Despite extensive studies in the past decades, a high-resolution structure of a LaTX is not yet available and the precise mechanism of LaTX action remains unclear. Here, we report cryoEM structures of the α-LCT monomer and the δ-LIT dimer. The structures reveal that LaTXs are organized in four domains. A C-terminal domain of ankyrin-like repeats shields a central membrane insertion domain of six parallel α-helices. Both domains are flexibly linked via an N-terminal α-helical domain and a small β-sheet domain. A comparison between the structures suggests that oligomerization involves major conformational changes in LaTXs with longer C-terminal domains. Based on our data we propose a cyclic mechanism of oligomerization, taking place prior membrane insertion. Both recombinant α-LCT and δ-LIT form channels in artificial membrane bilayers, that are stabilized by Ca2+ ions and allow calcium flux at negative membrane potentials. Our comparative analysis between α-LCT and δ-LIT provides first crucial insights towards understanding the molecular mechanism of the LaTX family.Latrotoxins (LaTXs) are presynaptic pore-forming neurotoxins found in the venom of  Latrodectus spiders contains seven Latrotoxins (LaTXs), among them α-latrocrustatoxin (LCT) and δ- latroinsectotoxins δ-LIT. LaTXs bind to specific receptors on the surface of neuronal cells and target the molecular exocytosis machinery. Here, the authors present the cryo-EM structure of the α-LCT monomer and the δ-LIT dimer, which reveal that LaTXs are organized in four domains and they discuss the potential oligomerisation mechanism that takes place before LaTXs membrane insertion. Both recombinant α-LCT and δ-LIT form channels in artificial membrane bilayers, that are stabilized by Ca2+ ions.The venom of  The vertebrate-specific α-LTX causes a clinical syndrome named lactrodectism upon a venomous bite to humans, which is fortunately rarely life-threatening but often characterized by severe muscle cramps and numerous other side effects such as hypertension, sweating, and vomiting6.Latrotoxins (LaTXs) are potent high molecular weight neurotoxins from the venom of black widow spiders. The venom contains an arsenal of phylum-specific toxins, including one vertebrate-specific toxin, α-latrotoxin (α-LTX)8. Most of the physiological and molecular biological researches to date have been carried out using the vertebrate-specific toxin α-LTX.LaTXs are produced as ~160 kDa inactive precursor polypeptides in venom glands and secreted into the gland lumen. There the final mature 130 kDa toxin is produced by proteolytic processing at two furin sites and cleavage of a N-terminal signal peptide and a C-terminal inhibitory domain2+ influx, thereby mimicking physiological voltage-dependent calcium channels10. Ca2+ influx activates the exocytosis machinery11 and triggers a massive release of neurotransmitters. α-LTX was shown to form also pores on artificial lipid bilayers, which have high conductance for monovalent and divalent cations such as K+, Na+, Ca2+, and Mg2+, but are blocked by transition metals and trivalent ions such as Cd2+ and La3+,16. Efficient incorporation into biological membranes strictly relies however on the presence of specific receptors19.α-LTXs have been shown to form cation-selective pores upon binding to specific receptors on the presynaptic membrane and induce Cai.e., the cell adhesion protein neurexin (NRX) which binds to the α-LTX in a Ca2+-dependent manner22, the G protein-coupled receptor latrophilin 24 and the receptor-like protein tyrosine phosphatase σ (PTPσ)25. With regard to α-LTX, NRX and PTPσ are suggested to provide only a platform for binding and subsequent pore formation events29. In contrast, Ca2+-independent binding to LPHN does not involve oligomerization and channel formation, but direct downstream stimulation of the synaptic fusion machinery32.To date, three receptors for α-LTX have been isolated, N4C,33, which lacks the ability of pore-formation but retains the full binding affinity to receptors, played a key role in the investigations of α-LTX action. Today, α-LTX is an indispensable tool for stimulating exocytosis of nerve and endocrine cells36. α-LTXs are furthermore considered to antagonize botulinum poisoning and attenuate the neuromuscular paralysis via synapse remodeling37. The surprising structural homology of α-LTX to the glucogen-like peptide-1 (GLP1)-like family of secretagogic hormones might also open opportunities for pharmacological applications in blood glucose normalization and reversal of neuropathies38.The channel-dependent and independent functions of α-LTX have attracted the attention of neurobiologists for several decades, studying the effects of α-LTX on neurotransmitter release and mechanisms underlying synaptic plasticity. The α-LTX variant LTX39. To date, four LaTXs have been cloned, including α-LTX7, α-LIT40, δ-LIT41, and α-LCT42. Despite their high specificity, the different LaTXs display a 30–60% sequence identity and are expected to share an overall similar domain organization and membrane insertion mechanism. Low-resolution 3D maps (14–18 Å) of the α-LTX dimer, α-LTX tetramer, and δ-LIT monomer were previously determined using single-particle negative stain and cryoEM43, suggesting indeed an overall similar architecture of the different members of the LaTX family.Invertebrate LaTXs are less well understood, but considered as promising candidates for the development of novel bio-pesticides. Orthologues of the three receptor classes shown to bind α-LTX are also present in insectsA structural and mechanistic understanding of LaTX function is a significant priority for the development of novel anti-toxin therapeutics and/or insecticides. However, a high-resolution structure of a LaTX, which is a prerequisite for the understanding of LaTXs’ mechanism of action at molecular detail, has been missing. Here we present a 4.0 Å cryoEM structure of the α-LCT monomer and a 4.6 Å cryoEM structure of the δ-LIT dimer revealing the molecular architecture of LaTX neurotoxins as well as the molecular details of their oligomerization mechanism prior to membrane insertion. In addition we characterized the principal basic pore characteristics of α-LCT, the precursor δ-LIT and δ-LIT channels after reconstitution into planar lipid bilayer.Latrodectus tredecimguttatus (Mediterranean black widow) (UniprotKB ID: Q9XZC0 (LCTA_LATTR))  from R)) Fig. . The cryR)) Fig. .Fig. 1StSubsequent image processing and 3D classifications revealed an inherent flexibility between both regions. The N-terminal head is orientated perpendicular and in close vicinity to the tail of the AR region in the best-resolved class (compact conformation), but shows a continuous movement and is tilted away the tail of the AR-domain in less well-resolved classes, resulting in less compact conformations  ; a central helical bundle domain (residues 116–352), and a short β-sheet domain, linking the helical bundle domain with the AR domain, termed here as plug domain (residues 353–452) , encircling a central α-helix (H8) Fig. . The conThe long and curved C-shaped C-terminal AR domain consists of 22 ankyrin-like repeats, accounting for two thirds of the sequence of α-LCT. In total, the first 18 out of 22 ankyrin-like repeats (ARs) were resolved in the present map. Interestingly, there is a redirection of orientation of the ARs at the loop (residues 890–902) connecting AR13 and AR14. ARs14-18 are rotated almost 90 degrees compared to ARs1-13 along the long axis of the domain Fig. . This arThe connector domain at the N-terminal end forms the lower interface connecting the central helical bundle with the AR-domain Fig. . HelicesThe plug-domain covalently links the primary sequence of the helical bundle with the AR-domain, positioning the helical bundle directly below AR1 (upper interface) Fig. . The plu45. To provide further insights into the LaTX family, we then focused on the insecticidal δ-LIT ). As expected, mature δ-LIT was toxic for our insect cell cultures and therefore, we expressed, purified, and subjected to cryoEM analysis the precursor uncleaved inactive toxin  in protomer A and  in protomer B, the last (15th) AR and the complete signal peptide and inhibitory domain in both protomers Fig. . These sThe δ-LIT dimer is formed with the two protomers rotated 90 degrees relative to each other and the plug domain of protomer A plugging from the side into a cleft formed by ARs 1-6 of protomer B Fig. . The proN4C with an insertion of a thrombin site (VPRG) at the linker peptide connecting the plug domain with the AR-domain was first introduced to functionally characterize the N- and C-terminals individually33. Although cleavage was not successful, this LaTX variant was shown later to retain its binding affinity to receptors but lose its ability to oligomerize into tetramers and form pores27. This variant played a key role in understanding the dual mode of action of LaTXs. The corresponding position of this insertion is highlighted on the molecular model of δ-LIT  shows a different, rather extended conformation and a larger distance between the helical bundle- and the AR-domain. Here, we used the model obtained by the best resolved δ-LIT density ), for direct comparison with the α-LCT. In particular, the outermost helix H9 and the overall helical bundle domain are straightened, the helical bundle of each protomer is further tilted 15 degrees away from the long axis of the AR-domain and the distance between the helical bundle domain and the shorter AR-domain is substantially longer. Notably, the AR-domain of δ-LIT is significantly less curved Fig.  and its The enlarged distance between AR-domain and helical bundle domain, also requires a more extended conformation of the connector domain, which is further stretched in δ-LIT, but still bridges both domains. This results into repositioning of H1 and unfolding of the lower half of H4 of the connector domain Fig. . These dIn general, α-LCT appears more compact, due to the seven additional terminal ARs, possibly allowing additional interactions between the AR-domain and the flexible central helical bundle. This might explain the different oligomerization properties observed for both molecules under identical cryoEM conditions. The directional change observed in the AR-domain of the extended δ-LIT protomer, would also result in the exposure of the helical bundle domains, even for LaTXs with significantly longer AR-domains such as α-LCT or α-LTX  and after the AR-domain (residue 1019) followed by proteolysis treatment after expression  150/25 mM KCl buffer conditions, the reversal potential (Vrev) was Vrev=4 ± 1 mV (SD of the linear i/v-curve fit) yielding 48, demonstrating only marginal selectivity to 2 (cis/trans)) gradient in symmetrical 150/150 mM KCl buffer, we observed an approximate five-fold increase in the reversal potential (i/v-curve fit)  Fig. ) reveali2+ ions to the cis side of the bilayer changed however the properties of the mature δ-LIT channel completely. The asymptotic-sine course of the current–voltage relation increased at both negative and positive command voltages (Vcmd) while crossing the zero voltage with zero net current . It turned out that the course of the current–voltage relation in the lower panel of Fig. Vcmd are carried from cis to trans mainly by Ca2+ ions and at positive Vcmd predominantly by Cl− ions  significantly smaller than the one calculated for the precursor δ-LIT channel  from cis to trans. In this context, it seems important that both the precursor δ-LIT and the mature δ-LIT are incorporated into the bilayer predominantly in a unidirectional manner, with a regulative Ca2+-binding site on the cis side. Strong rectifying current–voltage relations were observed in all bilayer experiments in the presence of Ca2+ ions added on the cis side    according the 2D class averages of the top view of the tetramer. The resulting 3D model of the tetramer is approximately 140 × 140 Å large and has a height of approximately 120 Å, with an overall striking configuration resembling a four-finger crane claw, with each curved AR-domain resembling a finger of the crane claw Fig. .Fig. 5La43. The putative transmembrane helices are still however completely shielded from the aqueous environment within the respective helical bundle domains. Nevertheless, the bottom part of the helical bundle domains is exposed in this arrangement. We therefore propose that the tetramer, shown here, composed of extended protomers, resembles an insertion-competent prepore state of the toxin.In the resulting model, the four cylindrical helical bundle domains are surrounding a central 10 Å diameter channel, which agrees excellent with the 2D class averages of the soluble δ-LIT tetramer Fig.  and the 43) and there are severe clashes between the AR-domains of adjacent subunits. Note that in previous 2D class averages of α-LTX tetramers with 22 ARs43, the central helical bundle domains are also exposed, suggesting that the single protomers are in the extended conformation. This suggests that α-LCT has to undergo a conformational switch from the compact to extended conformation during the oligomerization process and not after formation of the tetramer. Low-resolution 3D classes of the α-LCT monomer resembling intermediate states between the compact and the extended conformation further support this scenario  only in cryoEM samples  in an indirect way, since a complete LaTX tetramer is the prerequisite of functional pore-formation and thus enabling measurements of single channel currents. Indeed, our samples showed full activity and ion-channel gating events of single pore units were detected under the conditions of the high-resolution electrophysiological experiment. This is in complete agreement with previous experiments on α-LTX purified from the venom, which was also shown to form pores on artificial lipid bilayers, but efficient incorporation into biological membranes was only achieved in presence of specific receptors56, but the receptor-binding site  on LaTXs is still unknown. On the one hand, the helical bundle, connector and plug domains are buried deeply in the crane claw-like tetramer and the empty space below the helical bundles is most probably required for subsequent pore formation events  amphipathic helices surround a hydrophobic central helix. Moreover, an α-helix in Cry toxin domain I is also interrupted by a short loop+ or Ca2+ specific channels with comparable ion concentrations64. The LaTX pore is stabilized by Ca2+ and has a preferred permeability for this ion, suggesting binding sites in the LaTX pore specialized for Ca2+ sensing. A flexible loop in the pore can match both requirements: four loops might form an ion filter-like structure through coordinate bonds with a Ca2+, which can further facilitate the intake of the following Ca2+. In the absence of Ca2+, the loops become disordered, as observed for KcsA in the absence of K+ ions in the filter region65, but the pore might be then large enough to pass through the other substrates. Aspartic acid (Asp) and glutamic acid (Glu) residues have been known to play a critical role in Ca2+ filtering66. There are three Asp residues and five Glu residues strictly conserved at the helical bundle domain: Asp232, Asp289, Asp427, Glu121, Glu132, Glu146, Glu185, and Glu203 (residue numbers according to δ-LIT).It is known that LaTX pores are permeable for cations and small molecules such as ATP or acetylcholine. At the given ionic conditions the presented electrophysiological measurement further reveal conductance values that are in the order of magnitude as typically observed for K+ or Ca2+) at the extracellular side of the cell membrane, which might be crucial to orientate the molecules properly, with the claw tips directly facing the membrane. LaTXs oligomerize in a cyclic sequential manner, with four protomers rotated 90 degrees relative to each other. In particular, the individual protomers dock into each other, via the insertion of the plug domain of one protomer into the cleft formed by the AR-domain of the adjacent protomer towards the formation of the tetramer. For α-LCT  with protomers in the more compact state due to additional interactions between the helical bundle domain and the longer AR-tail, this interaction is expected to trigger in addition a conformational change for each protomer and stabilize the extended conformation. The resulting tetramer resembles in shape an open crane claw . Notably, in this orientation, the bottom part of the cylindrical helical bundle domains, composed each of five parallel aligned helices protecting the central putative transmembrane helix, are exposed, perpendicular aligned and directly facing the membrane. Even though the alternative possibility  cannot be completely excluded, this scenario is more reasonable considering the bipolar charge of the AR-tail and the overall arrangement of the putative transmembrane domains  and full-length δ-LIT  were optimized for recombinant protein expression in insect cells and fused with an N-terminal honeybee melittin signal peptide (MKFLVNVALVFMVVYISYIY)67 to enhance the efficiency of secretion. A Strep-tag II followed by an HRV-3C protease cleavage site was added after the melittin peptide, and a C-terminal His8 was added with a thrombin cleavage site. All DNA fragments were synthesized (GenScript Biotech) and cloned into a pACEBac1 plasmid68. For producing the mature δ-LIT, Domain I (residues 1–28) was deleted from the construction, and a TEV protease site was inserted between Domain III (CTD) and Domain IV (after residue 1019). The sequences of primers used for deleting Domain I and introducing TEV protease site were shown in Supplementary Table Our expression protocol was modified from Volynski K.E., et. al.E.coli cells. Positive baculovirus genomes were selected using blue/white screening on LB-agar plates containing 100 μg/ml Ampicillin, 10 μg/ml Gentamicin, 10 μg/ml tetracycline, 500 μg/ml 5-Bromo-4-chloro-3-indolyl-β-D-galactopyranoside , and 0.5 mM Isopropyl β-D-1-thiogalactopyranoside (IPTG). The white single colonies were inoculated into 5 ml LB containing 10 μg/ml Gentamicin at 37 °C for 24 h. The E.coli cells were lysed by the P1, P2, and N3 buffers of Plasmid Miniprep Kit (QIAGEN), followed by isopropanol precipitation. 700 μl isopropanol was applied to 800 μl cell lysate. The pellets were collected by centrifugation at 16,000 g for 10 min, washed with 200 μl 70% (v/v) ethanol, and solubilized in 50 μl sterilized water. The final concentration of bacmid was approximately 3 μg/μl.The bacmids were generated by transforming 200 ng of each plasmid to DH10EMBacY 6 cells/ml Sf9 cells and the supernatants, namely the V0 virus stocks, were collected after incubation at 27 °C for 72 h. In order to obtain the virus stocks in higher titration and larger amount, V1 and V2 virus stocks were generated by suspension culture of the 1.0 × 106 cells/ml Sf9 cells infected with 0.2%(v/v) virus from the previous step at 27 °C in 100 rpm for 72 h and stored at 4 °C with 10% fetal bovine serum (FBS) (Thermo Fisher Scientific). The titration of the final V2 virus stocks was approximately 6.0 × 108 PFU/ml measured by plaque assay.For generating the virus stocks, 10 μg of each bacmid were added with 250 μl Sf-900 II serum-free medium (Thermo Fisher Scientific) and 4 μl of FuGENE HD Transfection Reagent (Promega). The mixtures were transfected into 3 ml of 0.5 × 106 cells/ml Hi5 cells were infected with the V2 virus at a multiplicity of infection (MOI) of 1-2. After suspension culture at 27 °C in 100 rpm for 72 h, the Hi5 cells were removed by centrifugation. The supernatants containing the secreted proteins were chilled to 4 °C and adjusted to pH 8.0 by adding Tris. All the following procedures were conducted at 4 °C unless otherwise noted. White precipitation formed during pH adjustment and was removed by passing it through a 0.45 μm filter (Millipore).For large scale expression, 2.0 × 10Cleared supernatants were applied to gravity-flow columns filled with 10 ml of Strep-Tactin Sepharose resin (iba) and equilibrated in Wash Buffer ). Subsequently, the columns were washed with 50 ml (5 CV) of Wash Buffer and eluted with Elution Buffer . The elutes were fractionated and confirmed by SDS-PAGE. The fractions containing target proteins were pooled and concentrated to 0.5–5 ml. The protein samples were further purified by size exclusion chromatography (SEC) on Superdex 200 increase 10/300 or Superdex 200 26/60 column  equilibrated in Wash Buffer. The final concentration was measured by the Bradford method  and the purity was confirmed by SDS-PAGE and negative stain electron microscopy.The mature δ-LIT was generated by incubating the elution of Strep-tag affinity chromatography with 10% (w/w) His-PreScission 3C protease and TEV protease  at 4 °C overnight. The product was confirmed by SDS-PAGE followed by SEC as aforementioned.Negative-stain-EM was applied to all the three toxin samples to assess sample quality prior cryoEM analysis. 4 μl of each toxin sample with a concentration of 0.01–0.02 mg/ml were applied to a copper grid covered by a carbon layer and incubated for 2 min at room temperature. The excess protein solution was blotted with filter paper. The grids were washed with 10 μl double-distilled water and 0.75% uranyl formate once each, followed by staining with 10 μl 0.75% uranyl formate for 45 sec. Image acquisition was performed using a JEOL JEM-1400 transmission electron microscope operating at an acceleration voltage of 120 kV. Datasets were acquired with a 4k × 4k CMOS camera F416 (TVIPS) at a magnification of × 80,000. The defocus range was approximately −0.8 μm to −1.8 μm at a pixel size of 1.3 Å/px.For cryoEM sample preparation, 4 μl of protein solution were applied onto a freshly glow-discharged holey carbon grid (QUANTIFOIL R 1.2/1.3 mesh 300) and vitrified using a Vitrobot cryo-plunger (Thermo Fisher Scientific). The plunging condition was optimized by several rounds of screening sessions. The final concentrations of mature α-LCT and full-length δ-LIT solution were 0.6 mg/ml and 0.4 mg/ml, respectively.2. These two datasets were combined and used for the following processing.Two datasets of the α-LCT were collected with a 300 kV Titan Krios microscope (Thermo Fisher Scientific) equipped with an X-FEG and operated by the software EPU (Thermo Fisher Scientific). One image per hole with defocus of −1.3 to −2.5 μm was collected with the K3 Summit (Gatan) direct electron detector in super-resolution mode at a magnification of 105,000 x and a corresponding pixel size of 0.45 Å/px with a GIF quantum-energy filter set to a filter width of 20 eV. Image stacks with 60 frames were collected with a total exposure time of 3 s and a total dose of 69.1 e/Å2.Datasets of the δ-LIT were collected with a 300 kV Titan Krios microscope (Thermo Fisher Scientific) equipped with an X-FEG and a Cs corrector and operated by the software EPU (Thermo Fisher Scientific). One image per hole with defocus of −1.3 to −2.4 μm was collected with the K3 Summit (Gatan) direct electron detector in super-resolution mode at a magnification of 81,000 x and a corresponding pixel size of 0.45 Å/px with a GIF quantum-energy filter set to a filter width of 20 eV. Image stacks with 60 frames were collected with a total exposure time of 4 s and a total dose of 78.7 e/ÅThe details of the dataset collection are summarized in Supplementary Table 69, a recently released software package that allows automated on-the-fly processing for cryoEM. In particular, the motion correction was performed by MotionCor270 to create aligned dose-weighted averages. The super-resolution images were binned twice after motion correction to speed up subsequent processing steps. The non-dose-weighted average micrographs were used for CTF estimation performed by CTFFIND 4.1.1071. Outliers were removed based on the estimated defocus values and resolution limits. Further image processing was performed using the software package SPHIRE72 unless otherwise noted.Image transfer, motion correction, and CTF estimation were performed using Motioncor2 and CTFFIND4 implemented with TranSPHIRE73 based on a manually trained model and extracted with a final window size of 224 × 224 pixels. The 2D classification was performed by ISAC74 at a pixel size of 3.1 Å/pixel. The Beautifier tool implemented in SPHIRE was utilized for obtaining refined 2D classes. Classes displaying high-resolution features were selected and combined into a subset. For the α-LCT monomer, an initial 3D model was generated by RVIPER75 from a previously collected test dataset and subsequently used for 3D refinement in MERIDIEN72 with C1 symmetry. Since the resulting 3D reconstruction showed anisotropic resolution and several low-resolution regions, several rounds of 3D classification were performed by SPHIRE and RELION 3.177 until the compact and flexible intermediate classes were separated from the other conformations. The compact subset containing 376,474 particles was further improved by CTF refinement in RELION 3.1, followed by a final round of 3D refinement in MERIDIEN. The flexible intermediate class only contained 61,526 particles. Therefore, only standard 3D refinement was performed to generate a map for secondary structure fitting. The δ-LIT dimer dataset was processed with the same procedure as the α-LCT monomer until the 2D classification step. The window size was enlarged to 360 × 360 pixels due to the larger particle size. After sorting the monomer, dimer, trimer, and tetramer classes into subsets, a multi-reference classification of the dimer subset was performed in SPHIRE followed by an additional round of 3D classification in RELION 3.1. Eventually, a subset containing 81,192 particles was selected. The initial model for 3D refinement was generated by manually combining two α-LCT monomers, as indicated in the 2D class averages and applying a 15 Å low-pass filter. Subsequent polishing and CTF refinement were performed as described for the α-LCT dataset. The subset of monomer was further divided by 3D classification into two classes containing 70,924 and 41,599 particles. The initial model for 3D refinement was generated by the δ-LIT dimer map.Single particles were automatically picked by crYOLO78. The angular distribution and the local resolution distribution of the final map were analyzed using the o angular_distribution and sp_locres in SPHIREThe final half-maps were combined upon masking using a 3D mask generated by the PostRefiner tool implemented in SPHIRE, which in addition automatically determines the B-factor and filters the resulting volume to estimated resolutions. The global resolutions and the 3DFSC were calculated at the gold standard 0.143 criterion using the 3DFSC server79. Rigid-body fitting of the individual domain was then performed using Pymol . Further refinement was conducted using the real-time molecular-dynamics simulation-based program ISOLDE80 implemented in the visualization softwares UCSF Chimera and UCSF ChimeraX82, and the resulting model was further refined using a combination of the model editing software COOT83 and real-space refinement in Phenix84. The above adjustments were performed for several rounds until the model sufficiently fitinto the map. The model of the flexible intermediate state was generated by truncating side chains with the Chainsaw tool in CCP485 and fitted into the corresponding low-resolution map. The starting model of the δ-LIT dimer was generated by the homology-modeling service SWISS-MODEL86 from the α-LCT model, which was subsequently fitted into the map using ISOLDE, COOT, and Phenix. The disordered regions and side-chain atoms beyond Cβ in regions where side-chain density was only rarely evident were deleted during the model fitting. Rigid-body fitting into the low-resolution maps of this study  was performed with the molecular model of the compact state of α-LCT monomer (4 Å) for α-LCT maps and the protomer A of δ-LIT dimer (4.6 Å) for δ-LIT maps. Fitting was performed with Chimera and further refined using ISOLDE. Further model refinement was not applied due to the limited quality of the maps.The de novo model building of the compact form of the α-LCT monomer started from a 3D structure prediction using trRossetta (Yang 2020). The resulting model did not agree well with the density maps, therefore it was fragmented into several structural domains according to the secondary structure prediction from RaptorX Property82. The threshold values (sigma) were calculated by dividing the corresponding contour level by the root-mean-square (RMS) value. Multiple sequence alignment and the prediction of transmembrane helices were done using Clustal Omega87 and TMHMM server v2.088, respectively. The surface electrostatics was calculated in APBS89 and visualized by UCSF ChimeraX. These sequence features were illustrated by ESPript390 and Adobe Illustrator.To calculate the protein properties, i.e., surface electrostatics, hydrophobicities, residue conservations, and transmembrane region predictions, the side chains were put back with ideal rotamers  were used in cis and trans compartments. The denomination cis and trans corresponds to the half-chambers of the bilayer unit. Reported membrane potentials are always referred to the trans compartment. Bilayer fabrication was performed on PFTE film at a 100 µm prepainted (1 % hexadecane in n-hexane) aperture with a Phosphatidylcholine (18:1) (PC)/Phosphatidylethanolamine (18:1) (PE) (7:3 ratio) lipid mixture (both lipids were purchased from Avanti Polar Lipids) in n-pentane using the thinning method91. Stock solutions of purified LaTXs with typically 0.5–2.5 mg/ml contained in a buffer of 100 mM NaCl, 20 mM Hepes, pH 7.4 were added to the cis compartment under slight stirring to a final protein concentration of ~1 μg/ml to 10 ng/ml.Planar lipid bilayer measurements using the Compact bilayer platform (Ionovation GmbH) were performed as described in detail previously91.Ion-channel currents were recorded using an EPC 10 USB amplifier (HEKA Elektronik GmbH) in combination with the Patchmaster data acquisition software (HEKA Elektronik GmbH). For data acquisition a sampling rate of 5 kHz (voltage ramps) and 10 kHz (continuous recording) was used and the data were further analyzed using the Origin package (Origin Lab) and the MATLAB (MathWorks) based Ion-channel-Master software developed in our laboratory92. Besides the principal difficulties underlying the macroscopic GHK constant field theory, which assumes independent movement of the ions through membrane pores (see references95 for a detailed discussion) it has been demonstrated that the methodology can be used to obtain reliable semi-quantitative measures for permeation of charged drugs through membranes48.The Goldman-Hodgkin-Katz approach is by far the most commonly used framework to describe ion permeability and selectivity of membranesWe were interested in obtaining information on the selectivity of the membrane reconstituted δ-LIT. To characterize the ion fluxes mediated by the δ-LIT we employed the following experimental conditions for bilayer containing an unknown number of open δ-LIT channels.Permeability δ-LIT:Cation:Anion:Calcium:Zero-current potential:Ca2+:Cl1-:cis and trans compartment and considering that the assumptions of the GHK-theory are valid under the applied conditions we can use Eqs.  or Vmem)95. In summary, the course of the current–voltage relation in the lower panel of Fig. Vcmd are carried from cis to trans mainly by Ca2+ ions and at positive Vcmd predominantly by Cl− ions.Extrapolating the current slope at high positive and negative voltages linearly to zero net current Fig.  yields aGHK-current equations:46.Using Eqs. –13 we caFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Reporting summary


Adhesion G protein-coupled receptors (aGPCRs) form a sub-group within the GPCR superfamily. Their distinctive structure contains an abnormally large N-terminal, extracellular region with a GPCR autoproteolysis-inducing (GAIN) domain. In most aGPCRs, the GAIN domain constitutively cleaves the receptor into two fragments. This process is often required for aGPCR signalling. Over the last two decades, much research has focussed on aGPCR-ligand interactions, in an attempt to deorphanize the family. Most ligands have been found to bind to regions N-terminal to the GAIN domain. These receptors may bind a variety of ligands, ranging across membrane-bound proteins and extracellular matrix components. Recent advancements have revealed a conserved method of aGPCR activation involving a tethered ligand within the GAIN domain. Evidence for this comes from increased activity in receptor mutants exposing the tethered ligand. As a result, G protein-coupling partners of aGPCRs have been more extensively characterised, making use of their tethered ligand to create constitutively active mutants. This has led to demonstrations of aGPCR function in, for example, neurodevelopment and tumour growth. However, questions remain around the ligands that may bind many aGPCRs, how this binding is translated into changes in the GAIN domain, and the exact mechanism of aGPCR activation following GAIN domain conformational changes. This review aims to examine the current knowledge around aGPCR activation, including ligand binding sites, the mechanism of GAIN domain-mediated receptor activation and how aGPCR transmembrane domains may relate to activation. Other aspects of aGPCR signalling will be touched upon, such as downstream effectors and physiological roles. G protein-coupled receptors (GPCRs) are currently the most successfully targeted superfamily of receptors in modern medicine . GPCRs aWhat we know today as aGPCRs were first characterised in leukocytes in the 1980s. They were identified as the glycoproteins targeted by the mouse monoclonal antibody for F4/80, the mouse equivalent of the human GPCR EMR1 . F4/80, LNB-TM7, denoting its long N-terminal region, but also a close association with Class B1 GPCRs . Th. Th19]. aGPCRs are made up of two major components: N- and C-terminal fragments . The NTF encompasses most of the protein's ECD, comprising the GPCR autoproteolysis-inducing (GAIN) domain and a large, heavily glycosylated N-terminal region that varies in structure between each individual aGPCR and aGPCR sub-group. The CTF is C-terminal to the GAIN domain's GPCR proteolysis site (GPS), comprising the 7TM domain and an intracellular C-terminal tail .Due to the initial lack of endogenous ligands and modern techniques, initial exploration into the signalling of aGPCRs was slow. Despite having initially been placed into the Class B1 GPCR family, the large ECD lent itself to the ligand-binding site theory, like Class C GPCRs . HoweverAlthough initial experimentation was difficult due to the hydrophobic nature of the fragments, cleavage and subsequent treatment using stalk fragments was successful. Single amino acid changes in the post cleavage stalk-peptides were discovered to have a variety of responses in aGPCRs, including inverse agonism . This waThe N-terminal regions of aGPCRs are consistently longer than those of Class A GPCRs, hence their initial grouping with Class B1. Their high Ser/Thr content, with many being glycosylated, gives the region a rigid, extended structure with high solubility, similar to mucin. Hence, one of the first identified aGPCRs was termed the EGF module-containing mucin-like hormone receptor (EMR1/ADGRE1) . HoweverMany domains found in the aGPCR NTF are conserved features found in other proteins and across the aGPCR subfamilies  motifs are known to bind integrins and are notably found in ADGRE5 . Pentraxin vivo [Alternative splicing also expands this repertoire. The most variable part of aGPCR transcripts is the region N-terminal to the GAIN domain. Here, the position of individual domains can be altered, by addition of Ser/Thr stretches that vary NTF structure, or excluded entirely . This isin vivo .B subdomain . These residues form a sharp turn, created by a disulfide bridge located proximally to the GPS and the Leu R-group being trapped in a hydrophobic pocket. Proteolysis is achieved by nucleophilic attack on the L-T peptide bond by the Thr R-group, with the resulting ester hydrolysing to give two separate fragments of the original protein [The GAIN domain is found almost ubiquitously in aGPCRs, between the variable N-terminal domains and the 7TM region, with only ADGRA1 lacking this region . Its pri protein ,39,52.There is also evidence for ligands binding to the GAIN domain to trigger aGPCR activation, such as CD90 binding ADGRE5 ,53. Moreo [Cryo-electron microscopy (cryo-EM) was recently used to elucidate the first full-length structure of an active aGPCR (ADGRG3) in complex with small molecule agonists (glucocorticoids cortisol and beclomethasone) and Gαo . The reso ,56.6.53), contained within the UQC, recognises ligand binding and causes a conformational change that leads to its coupling to Gαo. Moreover, ADGRG3 lacks an ionic lock motif, normally found at the base of TM3 in Class A GPCRs (consensus: E/DRY), replacing it with a hydrophobic lock (HLY motif) that may perform similar roles in stabilising the receptor conformation on its cytoplasmic surface [Unlike Class A GPCRs ,58, ADGR surface . The difs dependent-manner as well as mobilise intracellular Ca2+ in a Gq/11 dependent-manner, resulting in neurite growth and neurogenesis [q mobilising intracellular Ca2+ [s stimulating intracellular cAMP production [GPCRs typically propagate their activation signal through two main classes of effectors: heterotrimeric G proteins, and β-arrestins resulting in an incredibly varied intracellular response profile . ADGRF1 ogenesis ,61. Intelar Ca2+  and Gs soduction . ADGRG2 β-arrestins are well known to mediate GPCR internalisation and activate numerous intracellular effectors for downstream signalling pathways, dependent on both the receptor itself as well as the ligand bound. Interestingly, β-arrestins can function in aGPCR signalling without full activation, which is atypical for many GPCRs . The preAs mentioned previously and reviewed extensively by Monk et al. , aGPCRs aGPCRs have been implicated in numerous diseases, in particular, various types of cancers where a lack of function results in increased cell growth and metastasis . One of aGPCR research is a rapidly changing field with many orphan aGPCRs and an emerging picture of how agonists cause receptor activation. Further insights into these may help in rational drug design for aGPCRs. This would aid in treatments of diseases which aGPCRs are involved in, such as cancer, due to their control over angiogenesis and up-regulation in breast cancer.The current consensus on aGPCR activation involves the GAIN domain acting in an autoinhibitory way to occlude a tethered ligand, as demonstrated by constitutively active aGPCR mutants. Ligands may bind to the NTF to cause the tethered ligand to be exposed, allowing its binding to the aGPCR orthosteric site and receptor activation. Recent cryo-EM studies have shown that, from here, aGPCR conformational changes reflect those in Class A GPCRs, but make use of different motifs.Future directions may include the cryo-EM analysis of more aGPCRs to allow comparative structural studies demonstrating the importance of sites such as the UQC; further elucidation of how the binding of ligands to the NTF can cause receptor activation; and using this newfound knowledge of aGPCR activation to design drugs to alter their activity.


Robust automated segmentation of white matter hyperintensities (WMHs) in different datasets (domains) is highly challenging due to differences in acquisition , population (WMH amount and location) and limited availability of manual segmentations to train supervised algorithms. In this work we explore various domain adaptation techniques such as transfer learning and domain adversarial learning methods, including domain adversarial neural networks and domain unlearning, to improve the generalisability of our recently proposed triplanar ensemble network, which is our baseline model. We used datasets with variations in intensity profile, lesion characteristics and acquired using different scanners. For the source domain, we considered a dataset consisting of data acquired from 3 different scanners, while the target domain consisted of 2 datasets. We evaluated the domain adaptation techniques on the target domain datasets, and additionally evaluated the performance on the source domain test dataset for the adversarial techniques. For transfer learning, we also studied various training options such as minimal number of unfrozen layers and subjects required for fine-tuning in the target domain. On comparing the performance of different techniques on the target dataset, domain adversarial training of neural network gave the best performance, making the technique promising for robust WMH segmentation. They are commonly found in elderly subjects, however, they have also been related to various neurodegenerative  and cerebrovascular diseases (e.g. stroke) . Automatdomains’ (e.g. different scanners or acquisition protocols). These include reducing the variance in the image-level characteristics , which eristics , which ceristics . Howeversource domain to a target domain by leveraging the invariant features across different domains (Domain adaptation (DA) methods address the issue of discrepancies in the data distributions obtained from various domains that affect the robust performance of the model . DA meth domains . Various domains , using t domains  and tran domains . Within  domains , self-la domains  and pseu domains  methods. domains . Hence, transfer learning (TL)  are fine-tuned on the target datasets. Fine-tuning of the pre-trained models has been shown to improve the performance on the target datasets (In ing (TL) , one of datasets , especiadatasets . TL has datasets , includidatasets . Howeverdatasets .unsupervised domain adversarial training , it has also been shown to be beneficial under semi-supervised setting (i.e. using a fraction of target domain segmentation labels)  .An alternative domain unlearning (DU) approach was proposed for domain and task adaptation using an iterative framework  and rec). The meIn this work, we explore various domain adaptation techniques such as TL and adversarial adaptation methods including DANN and DU for obtaining a good WMH segmentation across various datasets, and to perform well irrespective of differences in data characteristics. We used a triplanar ensemble network (TrUE-Net), proposed in our recent work  as a bas22.1Neurodegenerative cohort (NDGEN): The dataset, used in 3, median: 1424669 mm3. Manual segmentation was available for all datasets . The images were acquired using a 3T Siemens Trio Scanner, with FLAIR : The dataset consists of 18 participants in the OXVASC study : The dataset consists of 60 subjects from three different sources (20 subjects each) provided as training sets for the challenge (http://wmh.isi.uu.nl/): UMC Utrecht, NUHS Singapore and VU Amsterdam. The brain volume ranges: 1257820 - 1844920 mmhttp://wmh.isi.uu.nl/). Even though preprocessed images were available, we used the original images and applied the preprocessing pipeline specified in hallenge   in DU . This is2.4.6We trained the TrUE-Net model on the target training dataset and tested the model on the target test dataset. This case is expected to perform better than source-trained and other DA strategies on the target test dataset (for the given training options and data) since it is not required to cope with domain variance. Hence, this represents the case of upper limit for the performance metrics and is included for reference purpose only, since it does not improve model generalisability across domains.2.5We implemented the networks in Python 3.6 using Pytorch 1.2.0. The baseline network (TrUE-Net), for source-trained, target-trained and TL strategies , was trained on an NVIDIA Tesla V100, taking 5 mins (for 3 planes) per epoch for onfusion ). For de2.6source domain datasets, and the combination of OXVASC and NDGEN as our target domain datasets. Examples of the source and target domain datasets, along with the training/validation/test data split for above test strategies is shown in The datasets used in this work were acquired using different scanners and sequences and therefore have different intensity characteristics and resolutions. For performing our domain adaptation (DA) experiments, we classified the available datasets into two domains. Rather than considering each dataset as an individual domain, we considered only two domains (source and target) for our experiments. This is because the datasets have varying degrees of similarity among them and also, given the limited number of subjects for each dataset (for training and testing), treating them as individual domains would be difficult and give unreliable results. For deciding the source and target datasets, we determined the homogeneity of image-level characteristics among the above 5 datasets, using a domain discriminator network. To this aim, we trained a domain discriminator model on the above datasets and determined the domain misclassifications among these datasets using a confusion matrix  = 2 •Voxel-wise true positive rate (TPR), the ratio of the number of true positive WMH voxels to the number of true WMH voxels.•Voxel-wise false positive rate (FPR), the number of false positive WMH voxels divided by the number of non-WMH voxels.•Cluster-wise TPR, the number of true positive WMH clusters (determined using 26-connected neighbourhood) divided by the total number of true WMH clusters.•Absolute log-transformed volume difference (lAVD), which is defined by •Cluster-wise F1-measure = 2 We used the following performance metrics:For each metric we determined the significant differences between the individual pairs of test strategies, correcting for multiple comparisons using Permutation Analysis of Linear Models (PALM) .33.1As shown in 3.2The TL strategy results are reported for the setting  that was used for tuning training hyperparameters . With this setting, TL provided better performance than strategy 1 for all the evaluation metrics. However, it gave lower cluster-wise F1 measure values and higher lAVD values when compared to other DA models. The difference in cluster-wise F1-measure was significant when compared to semi-DANN, as reported in suppl. table S2.Later while determining the best setting for TL, the segmentation results improved with increased amounts of training data . For ins3.3In the case of unsupervised DANN, the segmentation was better than both strategies 1 and 2, as shown in 3.4In the case of semi-DANN, the addition of a fraction of the labelled target data to label prediction provided improvement in the segmentation performance over source-trained, TL and unsupervised DANN  . Also, oThe source-trained model applied directly to the target test dataset achieved the worst performance out of all strategies due to the differences in image resolution, pathology, intensity characteristics and lesion distribution, as shown in The TL strategy provided better performance metrics compared to the source-trained model. Adding even a few subjects (2 - 4 subjects) from the target domain slightly improved the performance metrics when compared to the model trained on source-dataset only (refer to suppl. section 6 for more details), even though they were not on par with using The performance of TL is better than training the baseline model on the combination of source and target dataset  on the target test dataset (even though the latter case shows better performance than strategy 1).The unsupervised DANN performed better than TL and the source-trained model. The DANN model extracted domain invariant features  and provided better visual results with less noise and more precise segmentation of boundary voxels. The simultaneous label prediction and domain unlearning with shared weights provides regularisation in the DANN model, thus avoiding over-fitting to the training data.The semi-DANN provided improvement over the DANN on the target test dataset. However, the DANN model detects less false positives when compared to the semi-DANN and provided comparable voxel-wise TPR values. Hence, while adding labelled target data might improve the performance of WMH segmentation in the target domain, it is necessary to weigh carefully the trade-off between improvement in the segmentation performance and the amount of manual effort involved, while choosing between unsupervised DANN and semi-DANN.The DU model performed better than the source-trained model and provided performance metrics on par with the TL strategy with higher cluster-wise F1 measure. While training the DU model, we observed that the factor for weighting the domain confusion, The target domain features are aligned closer to the source features with a good overlap after domain adaptation (as shown in the t-SNE plots). The semi-DANN showed the maximum overlap of source and target feature representations at the layer before label predictor. The better overlap of domains with semi-DANN (compared to the unsupervised strategy) is expected due to the introduction of the labelled target data for training the label predictor in the semi-DANN. Among the unsupervised techniques, DANN showed better overlap of the features when compared to the DU model. It is worth noting the performance of the adversarial training techniques (such as DANN and DU) depends mainly on the variations in lesion distribution and the acquisition characteristics between the source and target datasets (since they do not require lesion labels from the target dataset), rather than uncertainties in the manual segmentations on the target dataset (as in the TL case).The size of source and target domain dataset is an important factor that affects the performance of domain adaptation techniques. The model transferred from source to target domain in strategy 2, uses both source and target domain datasets for pre-training and fine-tuning respectively and hence learns the characteristics from both datasets. On the other hand, the difference in the source and target domain datasets’ sizes and the inhomogeneity in inter-/intra-domain characteristics especially affect unsupervised methods like DANN. To better investigate this aspect, we chose the two best performing adversarial adaptation techniques from our test strategies, DANN and semi-DANN, and trained them after swapping the datasets used for source and target domains. We observed that while DANN model is susceptible to slight changes in the performance , semi-DANN provided a consistent performance after domain swapping, without any significant difference in performance. For more details on the experiments and results, refer to suppl. We grouped the NDGEN and OXVASC datasets in the target domain according to their similarity (using a discriminator network), however these datasets still have differences in their characteristics . We therefore tested if the DA techniques performed differently in the two datasets and found no significant difference . This demonstrates the ability of DA techniques to learn the domain invariance between the target datasets.Concluding, we explored various DA techniques such as transfer learning and domain adversarial training techniques including DANN and DU. For the TL case, fine-tuning the intermediate layers towards the end of the encoder provided better results than fine-tuning the initial layers. The DANN models performed better than TL and the DU model on the target dataset. Particularly, the semi-DANN provided the best performance metrics with improvements over DU and TL cases. However, even without the addition of labelled target training data, the unsupervised DANN provided better cluster-wise and voxel-wise performance metrics compared to TL and DU, and results on par with the semi-DANN.Vaanathi Sundaresan: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft. Giovanna Zamboni: Supervision, Resources, Writing – review & editing. Nicola K. Dinsdale: Software, Writing – review & editing. Peter M. Rothwell: Resources, Writing – review & editing. Ludovica Griffanti: Conceptualization, Data curation, Supervision, Writing – review & editing, Project administration. Mark Jenkinson: Conceptualization, Supervision, Writing – review & editing, Funding acquisition, Project administration.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Mark Jenkinson receives royalties from licensing of FSL to non-academic, commercial parties.


The inherently heterogeneous microstructures of rocks lead to heterogeneity of the deformation distribution within the rock volume. In this study, experiments were conducted on red sandstone specimens with four different pre-existing crack inclinations stressed under uniaxial loading to investigate these features. Acoustic emission and digital image correlation techniques were used to confirm the damage process and obtaining deformation fields, respectively. The results showed that the heterogeneity of the deformation fields in the rock specimens amplifies with increasing stress magnitude, i.e., the displacement field heterogeneity shows two dense bands around the pre-existing crack, and the strain field heterogeneity shows localized regions with a numerical difference  and spatial concentration . The variations in the normalized standard deviation and spatial correlation coefficient were closely related to the damage process. The normalized standard deviation presented four evolution stages: relatively constant low value, steady growth, significant growth, and high rate growth. The spatial correlation coefficient changed from increasing at a relatively constant rate to increasing at a gentle rate and finally increasing rapidly. The evolution rate along with the strain showed two sharp fluctuations. The first could be used as precursor information of the damage. Finally, we confirmed the feasibility of the damage variable obtained from the heterogeneous deformation indicators used to calibrate or form damage evolution laws. Hence, various laboratory-based monitoring techniques have been used to obtain the salient features associated with the damage evolution process, such as the acoustic emission (AE), electromagnetic radiation (EMR), and infrared thermography9. The responses of the macroscopic stress–strain behaviors and deformation fields (displacement and stain fields) during the damage evolution of rocks are visible, and the deformation measurement in time and space is an essential procedure for analyzing the damage mechanism and fracture process in rocks. The multistage characteristics of a macroscopic stress–strain curve are typically used to identify the damage condition corresponding to the characteristic stresses, which are applied to guide engineering design, construction, and operation stability evaluation; for example, the CI stress (σci) between elastic and plastic transformations is considered for microstructure initiation14. The deformation field presents heterogeneity, characterized by the formation of localized regions with a high strain , where a high extensile strain is generated and fractures eventually occur17.The damage evolution in a rock,which is a heterogeneous natural geological material, proceeds with internal microstructure closure, initiation, propagation, and coalescence form the microscale to the mesoscale and lastly to the macroscale, with a series of physical parameters  exhibiting responses20. Various direct methods, such as X-ray imaging, scanning electron microscopy (SEM), and CT scanning, have been used to study the evolution associated with microstructure26. However, the aforementioned techniques have the following limitations: (1) The loading needs to be interrupted when analyzing the microstructure evolution; (2) The microstructural analyses are partial because a local region is selected in the experiments; (3) The experimental processes are complicated and expensive, and the large amount of data generated require considerable time for calculations. To overcome these drawbacks, some scholars28 developed numerical techniques to simulate the influence of microscale heterogeneity on the damage process of rocks. However, because of the many hypotheses made, the physical models established though numerical experiments are inconsistent with practical observations. In recent years, the digital image correlation (DIC) technique has been used as a feasible technique to investigate the evolution characteristics of the deformation fields and thus help understand the damage evolution in various types of rocks30. Because tensile mechanisms dominate the damage initiation and accumulation in rocks, the horizontal strain (εxx) field or shear strain (τxy) field extracted at the characteristic stress is typically selected to study the crack development in intact rocks or rocks with pre-existing cracks though uniaxial compression and tensile tests34, biaxial tests36, Brazilian splitting tests38 and shear tests40. In the DIC technique, the pixel points on the surface of rocks, typically in the order of thousands, serve as information carriers of the deformation, while only a few pixels with a high strain carry the damage information. Hence, some researchers42 calculated the dispersion degree of the deformation fields from a statistical perspective to explain the damage evolution in rocks.The heterogeneous deformation fields originate from the heterogeneous microstructure of the rocks33. However, there is scope to quantitatively assess the heterogeneous evolution of the deformation fields along with the damage. Accordingly, in this study, experiments were conducted on red sandstone with four different pre-existing crack inclinations stressed under uniaxial loading, in combination with AE and 2D digital image correlation techniques (2D-DIC) measurements to investigate the heterogeneous deformation fields. Further, two statistical indicators were newly proposed to characterize the numerical difference and spatial gathering in the heterogeneous deformation fields, and the evolutions of these indicators with the loading were explored corresponding to the damage process confirmed by the AE parameters. Additionally, the damage variable was initially quantized from the heterogeneity of the deformation fields, and the stress–strain relationship expressed by the aforementioned damage variable was proven to be feasible by continuum damage mechanics.Compared with physical information and microstructure evolution, the acquisition and appearance of heterogeneous deformation fields are more convenient, direct, and comprehensive in identifying the damage condition and precursory information43. X-ray diffraction (XRD) tests were performed to obtain the mineral composition of red sandstone: 63.5% quartz, 14.3% calcite, 12.4% feldspar, 5.5% clay minerals, and 4.3% other minerals. The grain size distribution was in the range of 0.04–0.4 mm. The density of the intact red sandstone specimens was 2.43 g/cm3, the uniaxial compressive strength was 87.13 MPa, the tensile strength was 5.52 MPa, and the elastic modulus was 8.31 GPa.In this study, red sandstone was chosen for the experiment owing to its homogeneous and isotropic naturel) and 2 mm in width (w) was carefully produced in each specimen using a water-jet cutter, and four different crack inclinations (α) were selected: 0°, 30°, 45°, 60°. Finally, a manual speckle field was made as follows: the rock cuttings on the specimen surface were swept, and the specimen surface selected for the deformation monitoring was sprayed with a thin coat of black matte primer. After drying, the white paint was randomly sprayed as speckle. Figure All the specimens were carefully cut into standard specimens 50 mm in length, 50 mm in width and 100 mm in height using a circular saw. Subsequently, a crack with 10 mm in length  on the material surface as the information carrier, matches the same pixel points in the reference image and so-called current images through correlation calculation, and obtains the relative displacement before and after deformation, thus realizing the strain measurement in the deformation field of the material surface. Figure tk) was taken as a sample, a series of Nx × Ny matrices with MATLAB was obtained, including the displacement and strain, representing the temporo-spatial evolution of the deformation behaviors, where Nx and Ny represent the numbers of rows and columns of the matrices, respectively. Let Sij(tk) and (tk) be the matrix element representing the deformation and position, respectively, at time tk corresponding to the ij-th pixel .Previous studies have shown that the displacement in the dense-ribbon region of the displacement gradient changes rapidly and converges at a position, and the high and low strain differentiation presents two-sided features: spatial gathering and numerical difference. Referring to the idea of statistics, the deformation field information obtained at a certain time , variance (VDF), standard deviation (SDDF), and variation coefficient(VCDF) of the heterogeneous deformation fields have been used in previous studiestk) can be expressed as follows:In this study, the normalized standard deviation (NSDDF) and spatial correlation coefficient (SCCDF) were proposed to characterize the heterogeneity of the deformation fields. The NSDDF is defined as the ratio of the standard deviation of the deformation fields to the maximum standard deviation: the higher the NSDDF, the greater the level of heterogeneity. The NSDDF  and cumulative event count (CEC). In this study, the ECR and CEC monitored in the tests were normalized and plotted in the same figures as the stress–strain curves. The relationship between the strain and time was linear : ε = at + b; the a, b parameters are listed in Table The AE emitted during the propagation and coalescence of microcracks is an effective method to detect the process of rock deformation and damageA typical record  was chosen for the analysis. As shown in Fig. 45, in this study, the evolution rates of the ECR and CEC were used to confirm the damage evolution in the red sandstone specimens with the four different pre-existing crack inclinations. As shown in Fig. According to previous studies on confirming the damage evolution in rock specimens using the AE methoduy) and shear strain field (τxy) of the specimen No. 0-1, No. 30-1, No. 45-1, and No. 60-1, corresponding to the markers A, B, C, D, E, and F, and the macroscopic fracture morphology (from left to right).The speckle images of the rock surface during the entire loading process were recorded in the test and then processed and analyzed using the DIC software. Further, the displacement and strain fields could be obtained. Six markers  at different stress levels were selected to analyze the temporal-spatial characteristic of the heterogeneous deformation fields. As shown in Fig. uy varied slightly without any evident gradient change; meanwhile, τxy did not show any noticeable change. In the linear elastic stage , due to the initial microcrack closure and lack of new microcrack, uy changed uniformly with a clear gradient at the bottom and top of the specimens, while at the middle of the specimen, the uy gradient was cut off by the pre-existing crack, and a serried trend could be observed at both ends of the pre-existing crack, where higher τxy gathered. In particular, at marker C, there was an evident partitioned dissimilation of the τxy strain field between the larger deformation points and the smaller ones. At marker D, the specimens had undergone plastic deformation with microcrack development, and the uy displacement field showed an evident heterogeneous change, i.e., two dense bands of the uy displacement gradient were distributed at both ends of the pre-existing crack. In addition, the direction of the uy displacement gradient reversed in the dense band and turned into a horizontal change, where the tensile effect appeared initially. Thus, the nonuniformity of the τxy strain field, i.e., the partitioned dissimilation between the larger and smaller strain points, was distinct at both ends of the pre-existing crack. With the increase in loading , the dense band of the uy displacement continually developed, where the heterogeneity of the τxy strain field was reinforced. In the post-peak stage, the tiny cracks, covered with paint or not caught by naked eye, had already grown including tensile and shear cracks. As illustrated at marker F in Fig. uy displacement field increased beyond the threshold value. Compared with the macroscopic fracture morphology, the tensile cracks appeared in the larger τxy gathering zone at both ends of the pre-existing crack , As discussed above, the heterogeneous deformation information could be intuitively obtained from the displacement and strain fields of the rock specimens with the four different pre-existing crack inclinations. In comparison, the damage evolution process could not be quantitively described from the heterogeneous deformation fields.τxy strain points, taking specimen No. 60-1 as an example, its top 5%, 10%, and 15% of the τxy strain points were selected to calculate the SCCDF using the proposed method, and the SCCDF-strain curves were drawn. As shown in Fig. τxy strain points were selected for the SCCDF calculation.To confirm the suitable range of the Crack closure stage(I): Due to the low loading and the closure of the initial microcracks, the heterogeneous deformation of the rock specimens were not evident, and the NSDDF value was low and presented a relatively constant trend. However, a part of the larger strain points still gathered around the pre-existing crack, and the SCCDF kept increasing at a relatively constant rate.Linear elastic deformation stage(II): Because of the increasing deformation degree increasing at both ends of the pre-existing crack, the heterogeneous deformation was pronounced. Therefore, the NSDDF value increased, particularly in the later linear elastic deformation stage, increased quickly , and even exhibited a mutation . The SCCDF still kept increasing at a relatively constant rate, while it increased quickly in the later linear elastic deformation stage and subsequently exhibited a mutation.τxy strain field increased, thereby significantly increasing the NSDDF increased drastically. With the τxy strain gathering continuously at the end of the pre-existing crack, there was little change in the spatial correlation, and the SCCDF increased at a gentle rate.Plastic deformation stage(III): With microcracks developing in the specimens, the heterogeneous degree of the τxy strain field increased continuously, and the NSDDF exhibited a second mutation at the peak stress and subsequently kept increasing at a high rate up to the maximum. Also, the spatial correlation of the larger strain points increased, and the SCCDF increased quickly and exhibited a second mutation.Post-peak stage(IV): After the peak stress, the macroscopic fracture formed at the larger strain points development area, the heterogeneous degree of the Based on the calculation method described in Sect. The damage development process of the rock specimens with four different pre-existing crack inclinations could be derived from the heterogeneous deformation. The variations in the NSDDF and SCCDF were closely related to the rock damage; thus, an acceptable agreement was found between the heterogeneous deformation behaviors of these specimens.τxy strain field and showed a specific evolution characteristic at each damage stage. In particular, in the transformation from elasticity to plasticity and at the peak stress, these values increased drastically to varying degrees (similar to the AE parameters). In other words, the evolution rates of the NSDDF and SCCDF were sensitive to key events in the crack development process of the rock specimens. Furthermore, taking the derivative of the normalized standard deviation and the spatial correlation coefficient in the strain field using the finite difference method, the evolution rate (v) could be obtained as follows:vi(tk) is the evolution rate at time tk; Si-1 and Si+1 are the NSDDF or SCCDF value at times tk-1 and tk+1 respectively; Δt is the time interval, Δt = tk-tk-1.As mentioned earlier, the NSDDF and SCCDF expressed the heterogeneity of the v of the NSDDF was almost constant at the crack closure stage. While in the linear elastic deformation stage, it fluctuated gently, and fluctuated sharply near the plastic deformation stage . Subsequently, the fluctuation in the NSDDF evolution rate changed weakly, and it fluctuated sharply again in the peak stress later . The first sharp fluctuation  can be used as precursor information of rock fracture, corresponding to the crack initiation stress. As shown in Figs. 9a2, b2, c2, and d2, v of the SCCDF also fluctuates sharply near the plastic deformation stage , and fluctuates sharply again in the peak stress later, marker P3 could also be used as precursor information of rock fracture.For all the rock specimens , 48. Considering the degree and spatial characteristics of the heterogeneous deformation fields, the damage could be quantified from the heterogeneity of the deformation field using the following relationship:D is the damage variable.As discussed previously, the AE signals form the rock specimens increased before sudden failure with the loading, and the strain field showed heterogeneity, indicating the formation of localized regions with numerical difference and spatial gathering. The former could be attributed to the high-frequency elastic waves generated during the development of microcracks in the specimens, whereas the latter was the outward manifestation of the microcracks in the specimen. In other words, there was a similarity between the two in terms of the generating mechanism. Both the AE and heterogeneous deformation field can help describe the damage evolution in the rock specimens. Damage quantification from the AE parameters has been discussed in previous studiesFigure 49, the effective stresses during uniaxial compression can be written as:h is a microdefects closure parameter that is material-dependent, but most often h ≈ 0.249.Using the concept proposed by Lemaitre and DesmoratE0 is the slope of the linear elastic deformation stage 15) and tCombining the above observations, it can be concluded that the NSDDF and SCCDF showed evident multistage characteristics and could capture the heterogeneous deformation abnormalities during the damage process by making full use of the full-field deformation information.uy displacement field showed two dense bands around the two ends of the pre-existing crack, where the uy displacement gradient reversed, and a tensile effect appeared. In the same regions, the crack-induced heterogeneity of the τxy strain field presented numerical difference and spatial concentration, i.e., a high extensile strain was generated and finally fractures occurred.The crack-induced heterogeneity of the The statistical characteristics quantified by the NSDDF and SCCDF were closely related to the stress–strain state. At the four damage evolution stages, the NSDDF was found to present a relatively constant lower value, a steady increasing trend, a dramatic increasing trend, and increasing with a high rate. Its evolution rate showed two sharp fluctuations near the plastic deformation stage and peak stress; the SCCDF increased at a relatively constant rate, increased at a gentle rate, and increased quickly, also showing two sharp fluctuations. The sharp fluctuations in the NSDDF and SCCDF were reasonably consistent with the CI and peak stress levels of the four rock specimens, and the first sharp fluctuation could be used as precursor information of rock fracture.The damage variable could be quantified by utilizing the heterogeneous deformation indicators, and it occurred more gradually with increasing axial strain and pre-existing crack inclination. The theoretical stress–strain relationship expressed by the forementioned damage variable strongly matched the experimental stress–strain curves of the rock specimens.In this study, the crack-induced heterogeneity of the deformation field of red sandstone specimens with different pre-existing crack inclinations was experimentally characterized. The temporal and spatial distribution characteristics of the heterogeneous deformation fields were evaluated in real-time using the DIC method under uniaxial compressive loading. The statistical variations in the strain field of the four rock specimens were also analyzed to achieve the main objective of understanding how it varies in the four damage evolution stages. The following main conclusions can be drawn: