Pbase (the percentage of base area buried in the RNA interface). In view of the three RNA classes, more detailed analyses on protein–RNA interactions were comprehensively performed from various aspects, including interface area, structure, composition and interaction force, so as to achieve a deeper understanding of the recognition specificity for the three classes of protein–RNA interactions. According to our classification strategy, the three complex classes have significant differences in terms of almost all properties. Complexes in the high class have short and extended RNA structures and behave like protein–ssDNA interactions. Their hydrogen bonds and hydrophobic interactions are strong. For complexes in low class, their RNA structures are mainly double-stranded, like protein–dsDNA interactions, and electrostatic interactions frequently occur. The complexes in medium class have the longest RNA chains and largest average interface area. Meanwhile, they do not show any preference for the interaction force. On average, in terms of composition, secondary structures and intermolecular physicochemical properties, significant feature preferences can be observed in high and low complexes, but no highly specific features are found for medium complexes. We found that our proposed Pbase is an important parameter which can be used as a new determinant to distinguish protein–RNA complexes. For high and low complexes, we can more easily understand the specificity of the recognition process from the interface features than for medium complexes. In the future, medium complexes should be our research focus to further structurally analyze from more feature aspects. Overall, this study may contribute to further understanding of the mechanism of protein–RNA interactions on a more detailed level.Protein–RNA interactions are very common cellular processes, but the mechanisms of interactions are not fully understood, mainly due to the complicated RNA structures. By the elaborate investigation on RNA structures of protein–RNA complexes, it was firstly found in this paper that RNAs in these complexes could be clearly classified into three classes  based on the different levels of  Qualitative and quantitative measurements of the influence of structure and composition of RNA interfaces on protein–RNA interactions. Since PDIs have been widely reviewed before PRIs, insufficient structure data limit the further development of research on PRIs.8,9 With recent advances in biological technology, the number of available PRI structures are increasing, which provides an opportunity to launch a structure-based analysis on the principles governing the interactions between proteins and RNAs. These research studies on PRIs mainly include the construction of PRI databases,10,11 sequential or structural comparisons between PRIs and PDIs,12,13 prediction of RNA-binding sites,14–17 and structural dissection of protein–RNA interfaces.18–20Nucleic acids, including DNAs (deoxyribonucleic acids) and RNAs (ribonucleic acids), always function through interactions with proteins. Such interactions play crucial roles in a wide variety of biological processes. Protein–DNA interactions (PDIs) are essential for DNA transcription, packaging, replication and repair.21–28 Bahadur et al.22 analyzed PRIs in terms of interface size, composition, polar interactions and atomic packing and found electrostatic complementation, base recognition and shape complementarity on the interfaces of PRIs. By investigating the preferred RNA structural states in protein-binding regions, Gupta et al.24 observed strong preferences for both RNA bases and RNA structural states in protein–RNA interactions, indicating their mutual importance in protein recognition. The work of Iwakiri et al.26 suggests that nucleotide bases in the RNA loop are flipped out and form hydrogen bonds with the proteins, and different protein surface shapes prefer different RNA base-pairing properties. The most recent report by Barik et al.28 compared the structural, geometric and physicochemical properties of interfaces involved in protein–RNA, protein–DNA and protein–protein interactions. The result indicates that H-bonds, salt bridges and stacking interactions play significant roles in stabilizing PRI interfaces. Despite the great progress in PRI research, the structural mechanism underlying PRIs is still not fully understood, owing to the amazing diversity of RNA structures. Compared to double-stranded DNAs, RNA molecules display a much wider variety of conformations and shapes.29 Moreover, a nucleotide is composed of the negatively charged phosphate, neutral ribose and polar base. As we know, the properties of different RNA interfaces commonly determine the different interacting modes of RNAs with proteins. Therefore, the initial aim of our work is to qualitatively and even quantitatively measure the influence of the structure and composition of RNA interfaces on protein–RNA interactions.In recent years, much attention has been paid to examining the general interface properties of protein–RNA complexes.Pbase (the percentage of base buried area in the RNA interface) can be as high as over 80% or lower than 10%. According to the different values of Pbase, the 137 RNA interface structures can be clearly clustered into three classes . Then, in order to understand the recognition process specificity of the three RNA classes, a comprehensive feature analysis was implemented on interface structures, intermolecular physicochemical properties and interface forces. Systematic comparisons among the three classes of complexes suggest that their interfaces are obviously different in terms of most features, which shows that the classification of RNA interfaces based on Pbase is reasonable. We demonstrate that the interface area contributed by the RNA base group could strongly influence protein recognition and binding, indicating that it can be used as a new determinant to distinguish different types of protein–RNA complexes. Thus, our analysis may contribute to understanding the specificity of the recognition process and the identification of protein–RNA binding sites on a deeper level.Firstly, the relationship between composition and structure of the RNA interface was explored. We collected a non-redundant dataset of 137 X-ray structures of protein–RNA complexes and analyzed the contents of phosphate, ribose and base on each RNA interface. It was interestingly found that the three RNA groups show obvious composition differences among these 137 complexes, but the most significant difference is observed in terms of base composition. 2.2.130 (Feb 2014) with X-ray structures and resolution better than 3.0 Å as criteria. In the present study, we only extracted those protein–RNA complexes containing proteins with at least 20 amino acid residues and RNA molecules with at least 5 nucleotides (nt). Some PDB chains containing only Cα atoms were excluded from our dataset. Moreover, some ribosomal subunits and viral protein–RNA complexes were also ignored in our dataset because these complexes often contain a large number of amino acid residues on interfaces and most of the RNA interfaces on proteins have not been determined, which could lead to population bias. For each protein–RNA complex, we chose a representative and stable biological assembly using the PDBePISA tool.31 Thus the entire dataset consists of 487 complexes  database2.233 was used to calculate the solvent accessible surface (ASA) values. The interface area of a protein–RNA complex was calculated using the web-based tool PRince,34 which uses NACCESS with a probe radius of 1.4 Å and default group radii. The size of a protein–RNA interface area (IA) was estimated by subtracting the ASA of the complex from the sum of the ASAs of the individual subunits, as shown in In this paper, the software NACCESS14,19,35 a residue with at least one interface atom was always defined as the interface residue. Based on Pribose, Pphosphate and Pbase were calculated using the following equations:Here, the interface atoms are referred to as those that lose solvent accessibility and contribute to IA in a complex. In previous studies,2.336 to identify and classify the types of nucleotide pairs. In our study, paired nucleotides are defined as any of 12 families of base pairs,37 and the remaining nucleotides are considered unpaired. Then, we calculated the Rpair, which indicates the ratio of the number of paired nucleotides to all nucleotides. Rpair represents the degree of pairing of RNA. In a protein–RNA complex, a smaller Rpair indicates more single-stranded regions in the RNA.We used RNA view2.4Ri/s), amino acid composition (AAC), amino acid propensity (AAP), secondary structure composition (SSC) and secondary structure propensity (SSP).Here, six important interface properties were calculated to reveal the structural foundations of different complexes. They are the interface area (IA), the ratio of interface area to surface area  regions. The SSC is defined as follows:fssi is the occurrence frequency of a particular secondary structure type in the interface residue sets, and NSSi is the corresponding number of the secondary structure type. The SSP is calculated as follows:fSS, SURFi is the occurrence frequency of a particular secondary structure type in a protein surface.The program STRIDE2.540 and positively charged electrostatic patches on protein surfaces were obtained through BindUP.41 For each protein–RNA complex, we calculated the percent overlap between the largest electrostatic positive patches on protein surfaces and the RNA-binding interfaces of each chain (Pe) and the mean Pe (P̄e):42Noverlap is the number of the overlapped residues between positive patch and the binding interface, Ni is the number of interface residues, and n is the number of amino acid chains in each complex. P̄e reflects the electrostatic property of the interface. In addition, the exact electrostatic energy of each complex was calculated by the MM/GBSA approach43 using MMPBSA.py tools44 in the Amber16 package.45Here, five kinds of noncovalent interactions were considered, including hydrogen bonds, electrostatic forces, van der Waals contacts, hydrophobic interactions and stacking interactions. Hydrogen bonds (H-bonds) at protein–RNA interfaces were calculated using the software HBPLUS,46 van der Waals contacts are denoted as the sum of the van der Waals radii of the two atoms plus a maximum distance (defined ≤ 1.0 Å). Stacking interactions are defined as the π–π interactions that can occur between the side chains of Tyr, Trp, Phe, His and the bases. Moreover, we also considered the π–π and π–cation stacking of Arg through its guanidinium moiety onto nucleosides. Hydrophobic interactions are deemed as non-polar atoms that are ≤5.0 Å apart. We calculated the percent overlap between the hydrophobic interface and the RNA-binding interfaces of each chain (Ph) and the average Ph (P̄h):Nnonpolar is the number of overlapped residues between the hydrophobic interface and binding interface; P̄h can reflect the hydrophobic property of the interface.van der Waals contacts, hydrophobic interactions and stacking interactions were measured by the program ENTANGLE.3.3.1Pribose, Pphosphate and Pbase, respectively, for the initial dataset . As a result, high includes 33 complexes with the average Pbase value of 65% and standard deviation (SD) of 6.5%. Medium comprises 61 complexes (Pbase = 37% ± 6.2%), and low consists of 43 complexes  in high are single stranded in structure with Rpair = 0, while those in low are double stranded in structure (31/43), with Rpair value greater than 0.8. In medium, RNA structure is more complicated because of the widely distributed Rpair values. For RNA types, we consider the five common types , and other RNAs were deemed as ‘other’ type. The detailed information is listed in ESI Table S3.Indeed, previous studies have shown that protein interactions with the RNA ribose-phosphate backbone are more common than interactions with the bases.3QJJ, 1F7U and 3VYY). We found that most RNAs are in single-stranded form when interacting with proteins in the high class, which exposes the base groups of RNAs on the interfaces. So, we can explain why Pbase values are high in the high class. Meanwhile, those in low mostly use their stem or double-stranded regions to bind to proteins, so the Pbase values are lower than others. In medium, RNAs can have both single and double-stranded states to interact with proteins, so the Pbase values are medium.A diagrammatic structure analysis was also performed on the complexes in the three classes, and Fig. S13.2Here, we calculated the interface properties for the protein–RNA complexes in three classes. 3.2.149,50 From 2, 3808 Å2 and 2673 Å2 in high, medium and low, respectively. For the non-redundant dataset, the average IA is 3192 Å2, which is contributed by 54 amino acids and 20 nucleotides. 2 to 8000 Å2, except for two complexes  due to the four or five protein chains on their interfaces. About 50% of medium complexes have IA > 4000 Å2; however, the IA sizes of high and low complexes are from 2000 to 4000 Å2. In high and low class, the distribution of IA has a peak at 2500 Å2. In addition to the same peak at 2500 Å2, the distribution of IA in medium class has another peak at 5000 Å2, which is consistent with the previous report, giving two broad peaks at 2000 Å2 and at 4800 Å2 for whole protein–RNA interfaces.28 This result indicates that the second peak is mainly contributed by the medium class. So, in terms of IA, complexes in high and low are similar to each other, but they are obviously different from those in medium (P < 0.05).IA is deemed an important property for macromolecular interactions.Ri/s.51,52Ri/s in different types of protein–RNA interactions. It reveals that complexes in high class have the highest average Ri/s, with the average values of Ri/s declining from high to medium and then to low, showing a different trend from the observation on IA in Ri/s. So, in terms of Ri/s, complexes in high are significantly different from those in medium and low (P < 0.01).A stable interface needs not only a large IA but also high 3.2.22, 90 Å2 and 74 Å2 in high, medium and low, respectively. Compared with the protein–DNA complexes,53 the complexes in high behave like protein and single-stranded DNA complexes, with the IA of 130 Å2 for each interface nucleotide. However, those in low behave like protein and double-stranded DNA complexes with the IA of 68 Å2. Then, we calculated the correlation coefficients (R2) between the number of interface atoms and IA for each class. The results in R2 values in high, medium and low all much higher than 0.90 for both the interface RNA atoms and the interface protein atoms. This result is consistent with the previous studies,22,28,54 which have confirmed that whether the complexes are in the high class, medium class or low class, the correlation coefficients between the number of interface atoms and interface area are high in both the protein and the RNA components.The number of interface atoms and residues/nucleotides on the protein and RNA interface are respectively shown in R2 in R2 in R2 of 0.91, 0.93 and 0.76 in high, medium and low class, respectively. On the RNA side, medium class yields the minimum R2 (R2 = 0.59), while R2 is 0.88 and 0.70 in high and low class, respectively. This result may be due to the more complicated RNA structures of medium complexes. Previous studies have reported that the linear correlation between IA and the number of interface nucleotides is low, with R2 of 0.67.22,28 From our results, we can explain that the complexes in medium may be key samples for this mediocre correlation.The correlation between the number of interface residues/nucleotides and IA was investigated for each class. We found that the values of 3.2.313,19,22,29 Though in all classes, the AACs of positively charged amino acids are all relatively high, their preferred residues are different. In high, the largest contribution comes from Lys, while it is Arg in medium and low. Then, we calculated the total percentage of both Lys and Arg, and we found that the total percentage increases from high to medium, and then to low  and propensity (AAP) of the 20 amino acids on the interface residues in each class . Twenty 24 the non-regular elements are the primary protein interface structural state . The frequencies of different RNA bases involved in H-bonds are also different. U (14%) and G (10%) are more frequently found than A (7%) and C (8%). Interestingly, only in low class is the frequency of G (7%) larger than the frequency of U (2%), and this phenomenon has also been found in protein–DNA H-bonds.27,47 Overall, these results suggest that the complexes in high have the strongest hydrogen bonds, and H-bonds in low are similar to the protein–DNA H-bonds, both on the protein side and the RNA side.We also counted the frequency of all the chemical components for H-bonds in each class . On the 3.3.221 We studied the electrostatic force in each class from two aspects. Firstly, large positive patches are deemed an important property of protein surfaces, and they are usually considered a sign of binding interfaces.57–60 To investigate the electrostatic properties of the interface, we calculated the percent overlap between the largest electrostatic positive patches on the protein surfaces and the binding interfaces in each class , while in low class, it is maximum (64.9%), and in medium, it is also low (55.2%). Moreover, a similar trend is found on the average electrostatic energy of the three classes . Obviously, all the stacking interactions in high are stronger than those in medium and low. However, the contribution of residues and bases involved in stacking interactions is similar among the three classes. On the protein side, the side chains of Arg and Tyr are involved in more than 60% of all stacking interactions; however, the number of Trp involved in stacking interactions is least. Similar observations have also been found in the previous studies.28 On the RNA side, U and A are more frequently found than G and C in all stacking interactions. The number of stacking interactions contributed by purines  and pyrimidines  almost equal each other at 48% and 52%, respectively.The stacking interaction refers to attractive and noncovalent interactions between aromatic rings. These interactions are important in protein–RNA interfaces.25,28,46 and the contribution of residues and bases is similar in each class. Further, our results show significant differences in the strength of stacking interactions among the three classes. The complexes in high have the strongest stacking interactions, while in low complexes, they are very weak and RNAs may interact with the protein by other types of interaction forces.Overall, our results are consistent with previous studies using the same data set ,3.3.461 Our results suggest that there is no obvious difference among the three classes in terms of the van der Waals density on interfaces. Compared with H-bonds, it is easy to see that the van der Waals contact is a nonspecific force for each complex, so the preference of the RNA backbone and bases involved in van der Waals interactions is relatively weak.55,62The van der Waals contact is a basic intermolecular force which is closely related to the atomic spatial distance. We analyzed the number of the van der Waals forces in each class. Here, the main role of the van der Waals force is to stabilize the macromolecular structure, and the strength only depends on the atomic distances.3.3.523,63 We calculated the percent overlap between the hydrophobic and RNA-binding interfaces (P̄h) of each complex. The values are distributed between 0% and 100%, and the details are listed in ESI Table S2.P̄h into five categories by the values, which are 0–20%, 20–40%, 40–60%, 60–80% and 80–100%, respectively. The distributions of the five categories of P̄h in each class are shown in 46,62 In contrast to the phosphates and the bases, due to the presence of the 2′OH, it is harder for the ribose in RNA to form hydrophobic interactions.27To comprehensively explore the differences in interaction forces among our three classes, we finally analyzed the strength of hydrophobic interactions in each class. The hydrophobic interaction is one of the fundamental forces in the protein–nucleic acid interface.4.4–7 The different structures or conformations of RNA molecules may influence the binding protein sites.23,28 Moreover, the base is a special part of RNA and is frequently involved in important interactions.22,24,26,48 In the study, to qualitatively and further quantitatively measure the influence of the RNA composition on protein–RNA interactions, we firstly proposed a new standard to distinguish protein–RNA complexes based on the percentage of the base area buried in the RNA interface area. As a result, a dataset of 137 protein–RNA complexes was divided into three classes . We comprehensive analyzed the properties of protein–RNA interactions, including interface compositions, interface structures, intermolecular physicochemical properties and interface forces, and also analyzed the difference between the three class complexes as well as compared them with protein–DNA interfaces reported in previous research.12,13,47,53 The results are clear: complexes in high class have the shortest RNAs and the RNA structures are mainly single stranded, which facilitates the interaction of the flipped or exposed base group with proteins. These complexes behave like protein–ssDNA interactions. Among the five types of interactions, H-bonding and hydrophobic interaction are strong, while the electrostatic interaction is weak. The complexes in medium have the longest RNAs and the largest interface area; however, the interface ratio is the smallest. The linear correlation between IA and number of interface nucleotides is the worst because of the irregular and more complicated RNA structures. Meanwhile, the interaction forces do not show any preference. In low class, the interface area distribution is similar with that of high class. The RNA structures are mainly double-stranded and behave like protein–dsDNA interactions. The interface propensity of Lys is high. Compared to high class, the electrostatic interaction is strong, while stacking and hydrophobic interactions are very weak. According to our classification strategy, the three classes of complexes have significant differences in terms of almost all properties. Unlike the high or low complexes, we cannot easily understand the specificity of the recognition processes of medium complexes based on the interface features. Therefore, we would pay more attention to the medium complexes in the future. Moreover, it is necessary to develop specific predictors for complexes in different classes, and different classes of protein–RNA complexes should be studied individually. Our study proves that the size of interface area contributed by the RNA base group can highly impact the properties of RNA–binding proteins and may play an important role in understanding the mechanism of protein–RNA interactions.Protein–RNA interactions play important roles in a wide variety of biological processes.There are no conflicts to declare.RA-008-C8RA00598B-s001


Decades of research, in particular the discovery of new classes of non‐coding RNAs (ncRNAs), have unveiled a plethora of activities that RNAs can fulfil besides coding for proteins, ranging from catalysis over scaffolding to regulatory functions. These ncRNAs not only play important roles in healthy individuals, but also are implicated in a wide range of diseases, including cancers, cardiovascular and neurological diseases, which have been demonstrated in several clinical studies.Compared to classical genetic or pharmacologic perturbations, light is a very attractive actuator that can be used to manipulate cellular function and intracellular signalling pathways with millisecond and submicron resolutions.Unlike the extensive studies on developing optogenetic tools for transcriptional control, reports on optogenetic control of post‐transcriptional RNA metabolism are much fewer. Cao et al. developed optogenetic systems that can activate mRNA translation in mammalian cells in a light‐dependent manner. In these systems, blue light induces the reconstitution of an RNA binding domain and a translation initiation domain, thereby activating the translation of target mRNA downstream of the binding sites.Kd) of 92 nM, but in the dark, the binding is >40‐fold weaker with a Kd of 3793 nM. LicV can serve as an excellent scaffold for the development of photoswitchable RNA effectors that allow spatiotemporal control of the localization, splicing, translation and stability of the target RNAs, by simplify fusing LicV to RNA effectors with the corresponding functionalities. Furthermore, LicV‐based RNA effectors can be combined with CRISPR‐Cas system for highly efficient and tunable regulation of RNA synthesis from both exogenous and endogenous genes (see Figure In eukaryotic cells, RBPs are key components in RNA metabolism, regulating the temporal, spatial and functional dynamics of RNAs. Thus, the engineering of light‐switchable RBPs would provide an easier and more straightforward way to control the post‐transcriptional metabolism of RNA. Weber et al. selected an RNA aptamer that can specifically bind a natural blue light receptor, termed PAL, in a light‐dependent manner. PAL allows optogenetic control of mRNA translation both in bacteria and mammalian cells.Today, scientists’ interests in the contribution of RNA to the genesis and progression of human disorders are booming, but the functions of the vast majority of ncRNAs are still mysterious, and further studies are highly warranted to uncover their biological roles and the mechanisms by which ncRNAs exert their pathological effects. Light may be useful for precisely controlling the expression levels or subcellular localization of RNAs, thereby enabling scientists to link changes in cellular functions with changes in the intracellular availability of these RNAs. Despite the significant progress in developing RNA optogenetic tools, significant challenges still need to be overcome to expand the toolbox of RNA optogenetic tools for unlocking the deeper mysteries of RNA. First, previously developed optogenetic tools permit spatiotemporal control of a single RNA each time. However, many biological processes require the concerted action of multiple RNAs, for example, RNA splicing and ribosome assembly. To enable the precise control of these processes, it is necessary to engineer orthogonal and multichromatic optogenetic tools for simultaneous control of multiple RNAs. Second, most of the existing RNA optogenetic tools utilize blue light as the actuator, engineering of optogenetic tools that respond to red or infrared light would be more relevant for triggering RNA molecules in vivo, as light in this range is less toxic to cells and can penetrate tissues more efficiently than blue light. Third, optogenetic tools for the control of RNA epigenetics may also be engineered by fusing LicV to write or erase the functionalities of RNA methylation, as well as control of site‐directed RNA editing by fusing LicV to the deaminase domains. The combination of these optogenetic tools will provide an opportunity to control the functions of different RNAs differentially and orthogonally, which may be particularly useful for the study of RNAs with antagonistic or synergetic functions.All the authors declare that there is no potential conflict of interest.


Circular RNAs (circRNAs) are expressed and are regulated in many biological processes but little is known about their ability to directly control mRNA homeostasis. We show that circRNA zinc finger protein 609 (circZNF609) interacts with several mRNAs increasing the final protein levels, which in the case of the cytoskeleton-associated protein 5 (CKAP5) leads to a stabilized microtubule cytoskeleton and an enhanced tumor cell proliferation. Within this frame of research, growing interest is dedicated to circular RNAs (circRNAs), a well-established class of RNA molecules originating from a back-splicing event in which a downstream splice-donor site is joined to an upstream splice-acceptor site, yielding a covalently closed circular RNA. CircRNAs show several peculiar features, such as evolutionary conservation and tissue-specific expression, but above all, they have been found to be deregulated in many pathological conditions, including cancer. Several studies indicate that circRNAs elicit their function as miRNA sponges, but the cell-type-specific microRNA signature restrains this mechanism as a general mode of action. In our work3we explored the regulatory roles of the RNA-RNA interactions involving the circRNA zinc finger protein 609 (circZNF609), a circRNA previously reported to be regulated and to control human primary myoblast and embryonal rhabdomyosarcoma (ERMS) cell proliferation.5 CircZNF609 was also found to be upregulated in several types of human cancers, such as prostate cancer, breast cancer, gastric cancer, nasopharyngeal carcinoma, and hepatocellular carcinoma, where its depletion has been linked with reduced aggressiveness of tumor cells.The latest research showed the importance of RNA molecules as a key factor in the regulatory circuitries. To carry out these fine-tuning activities, RNA not only interacts with proteins and chromatin but often directly pairs with other RNA moleculesin vivo, we found that circZNF609 pairs directly with a few mRNAs, among which is the mRNA of the cytoskeleton-associated protein 5 (CKAP5). A more detailed molecular study points to the back-splicing junction of circZNF609 as the sequence responsible for this RNA-RNA pairing, underlining the specificity of this interaction in front of the linear ZNF609 mRNA counterpart. As a result, the CKAP5 mRNA is stabilized and its translation is enhanced. We demonstrated that this stabilization is produced by ELAV like RNA Binding Protein 1 , an RNA-binding protein (RBP) known to induce mRNA stabilization and translation and to control lncRNA metabolism.7 Specifically, circZNF609 contains several-binding sites for ELAVL1 in its sequence, and the interaction between the circRNA and CKAP5 mRNA will facilitate ELAVL1 loading from the circular onto the messenger RNA  ends, sustains their elongation and regulates their dynamicsCKAP5 mRNA interaction, through either small interfering RNAs (siRNAs) against circZNF609 or locked nucleic acid (LNA)-modified oligonucleotides against the pairing region, sensitizes rhabdomyosarcoma cells to microtubule-targeting chemotherapeutic agents. Moreover, we demonstrated that this regulatory mechanism is also conserved in other cancer cell models such as neuroblastoma and chronic myelogenous leukemia, setting such blockage as a potential therapeutic coadjuvant in cancer treatment.Microtubule dynamics is essential to all processes depending on the cytoskeleton, including cell migration and differentiation, the building-up of the mitotic scaffold and chromosome segregation. Several first-line chemotherapeutic agents, such as vincristine or Paclitaxel/Taxol, are designed to target either the microtubule assembly from tubulin or their dynamic activity; they are effective chemotherapeutic agents and are still in use to treat cancer of various origins, including rhabdomyosarcoma. We demonstrated that preventing the circZNF609/CKAP5 mRNA interaction, through either small interfering RNAs (siRNAs) against circZNF609 or locked nucleic acid (LNA)-modified oligonucleotides against the pairing region, sensitizes rhabdomyosarcoma cells to microtubule-targeting chemotherapeutic agents. Moreover, we demonstrated that this regulatory mechanism is also conserved in other cancer cell models such as neuroblastoma and chronic myelogenous leukemia, setting such blockage as a potential therapeutic coadjuvant in cancer treatment.Microtubule dynamics is essential to all processes depending on the cytoskeleton, including cell migration and differentiation, the building-up of the mitotic scaffold and chromosome segregation. Several first-line chemotherapeutic agents, such as vincristine or Paclitaxel/Taxol, are designed to target either the microtubule assembly from tubulin or their dynamic activity; they are effective chemotherapeutic agents and are still in use to treat cancer of various origins, including rhabdomyosarcoma. We demonstrated that preventing the circZNF609/CKAP5 mRNA interaction, through either small interfering RNAs (siRNAs) against circZNF609 or locked nucleic acid (LNA)-modified oligonucleotides against the pairing region, sensitizes rhabdomyosarcoma cells to microtubule-targeting chemotherapeutic agents. Moreover, we demonstrated that this regulatory mechanism is also conserved in other cancer cell models such as neuroblastoma and chronic myelogenous leukemia, setting such blockage as a potential therapeutic coadjuvant in cancer treatment.Microtubule dynamics is essential to all processes depending on the cytoskeleton, including cell migration and differentiation, the building-up of the mitotic scaffold and chromosome segregation. Several first-line chemotherapeutic agents, such as vincristine or Paclitaxel/Taxol, are designed to target either the microtubule assembly from tubulin or their dynamic activity; they are effective chemotherapeutic agents and are still in use to treat cancer of various origins, including rhabdomyosarcoma. We demonstrated that preventing the circZNF609/


RNA replication and transcription machinery is an important drug target for fighting against coronavirus. Non-structure protein nsp8 was proposed harboring primase activity. However, the RNA primer synthesis mechanism of nsp8 is still largely unknown. Here, we purified dimer and tetramer forms of SARS-CoV-2 nsp8. Combined with dynamic light scattering, small-angle neutron scattering and thermo-stability analysis, we found that both dimer and tetramer become loosened and destabilized with decreasing salt concentration, and the dimer form is more stable than the tetramer form. Further investigation showed that nsp8 dimer and tetramer can undergo phase separation but exhibit different phase separation behaviors. Nsp8 dimer can form liquid-like droplets in the buffer with a low concentration of NaCl; phase separation of nsp8 tetramer depends on the assistance of RNA. Our findings on different phase separation behaviors of nsp8 dimer and tetramer may provide insight into the functional studies of nsp8 in coronavirus. The phase separation behaviour of non-structure protein nsp8 of SARS CoV2 in the primer synthesis mechanism is presented, underpinning the replication of coronavirus. Despite extensive applications of vaccines, COVID-19 remains a threat to the global economy and health, due to the shortage of effective therapeutic agents against SARS-CoV-2. Considering the essential role in the life cycle of RNA viruses and the lack of homolog in host cells, RNA replication, and transcription machinery is an attractive therapeutic target against SARS-CoV-23. At least two RNA-dependent RNA polymerases (RdRp) are encoded by the coronavirus genome, including primer-dependent RdRp nsp124 and primer-independent RdRp nsp86. Recent structural studies on SARS-CoV nsp127 and SARS-CoV-2 nsp129 have characterized the RNA elongation mechanism of nsp12. It was proposed that nsp8 is capable of de novo RNA synthesis with an ssRNA template and then providing primers required for nsp125. Although structures of nsp8 from several coronaviruses were reported, the mechanism of nsp8 de novo RNA synthesis is still largely unknown.Infection of coronaviruses, a group of positive-strand RNA viruses, is a great threat to human health. The current pandemic coronavirus disease 2019 (COVID-19) is caused by a new coronavirus SARS-CoV-210. Recently, the viral factory was proposed to be a membrane-less organelle driven by liquid-liquid phase separation (LLPS)11. Concentrating reactive molecules in condensates via liquid-liquid phase separation was considered a powerful mechanism to accelerate biochemical reactions14. Proteins tended to LLPS were proposed to exhibit features including intrinsically disordered, modularity, nucleic acid binding, and oligomeric nature, et al.15. Sequence analysis showed that SARS-CoV-2 nsp8 contains a potential IDR  at the N terminus  as measured by DLS reflects the solvated protein size, while the radius of gyration (Rg) as measured by SANS reflects mostly the compositional distribution of hydrogenated protein molecules. The ratio of Rg to Rh (Rg/Rh) offers the shape information of protein molecules18. The larger Rg/Rh values of nsp8 dimers than those of nsp8 tetramers imply the variation of the shape of molecules from non-spherical to globular  and the onset temperature of aggregation (Tagg) indeed decreased at lower NaCl concentration for both nsp8 dimer and tetramer . Similar to the wild-type nsp8, nsp8ΔN76 also formed homodimer and tetramer in solution  with a sequence derived from the SARS-CoV-2 genome adjacent to poly (A). When mixing RED-tris-NTA labeled nsp8 tetramer (0.25 mg/mL) with R12, a liquid-like droplet could be observed with RNA concentration up to 75 μM  was over-expressed in Hela cells. By imaging this cell line using fluorescent microscopy, we observed that over-expressed nsp8 could form condensates in the cytoplasm, while over-expressed mCherry was diffuse in the entire cell Fig. . Then, w11. In coronavirus, the non-structure protein nsp8 not only is identified as a co-factor for nsp12 mediating RNA elongation, but also exhibits de novo RNA synthesis activity. In this study, we demonstrated that, SARS-CoV-2 nsp8 can undergo liquid-liquid phase separation in an oligomerization-dependent manner.Transcription and genome replication of viruses could be substantially amplified by concentrating replication and transcription machinery into a liquid-like structure17, which is in agreement with our current finding. However, Biswal et al. did not separate nsp8 tetramer from gel filtration analysis. During the purification process, we found that, when the concentration of NaCl in Ni-NTA purification buffer and gel filtration buffer is 300 and 100 mM, respectively, most nsp8 formed oligomers by associating with bacterial nuclear acid, only a small amount of nsp8 could be separated as a dimer, and nsp8 tetramer could not be identified. However, when the concentration of NaCl in the Ni-NTA purification buffer and gel filtration buffer increased to 1000 and 500 mM, respectively, we obtained nsp8 dimer and nsp8 tetramer. Based on our findings, we speculate that, the reason for Biswal et al. not detecting nsp8 tetramer through gel filtration might be that the NaCl concentration in the purification buffer may not be high enough.Recently, Biswal et al. observed tetramer form of nsp8 through crosslinking assay11. In this study, we identified the dimer and tetramer forms of nsp8, which undergo phase transformation at a low concentration of NaCl in aqueous solutions. However, they exhibit distinct phase separation behaviors: nsp8 dimers form separated liquid phases depending only on concentrations of the protein and salt, whereas nsp8 tetramers form solid-like aggregates at low salt concentration but can transform into liquid-like droplets with the addition of RNA. The concentration of NaCl is relevant to ionic strength, suggesting phase separation of nsp8 dependent on ionic strength. Thus, our results showed that LLPS of SARS-CoV-2 nsp8 depends on non-covalent modification and physicochemical changes in the environment. Intracellular ionic strength could be altered by cellular signaling events19. Further studies are needed to investigate if phase separation of nsp8 can be regulated by cellular signaling events affecting intracellular ionic strength.Droplet formation of the macromolecule can be modulated by non-covalent modification  or physicochemical changes in the environment 22. RNA-induced LLPS of N protein is dependent on the length of RNA23. Our studies on the phase separation of nsp8 tetramer showed that, its phase separation depends not only on the length of RNA, but also on the sequence of RNA. The physiological relevance of RNA sequence and length-dependent phase separation of nsp8 tetramer remains to be further investigated.More recently, SARS-CoV-2 nucleocapsid (N) protein was reported to undergo LLPS upon binding to RNA or SARS-CoV-2 membrane protein (M)21. Here, we determined that nsp8, a component of replication transcription complex (RTC), can form liquid-like droplets in solution and live cells. To our knowledge, nsp8 is the first component of the replication transcription complex in coronavirus that possesses LLPS property. It is interesting to see if nsp8 co-operates with N protein to amplify transcription and replication of SARS-CoV-2 via LLPS. More work is needed to investigate if the primase activity of nsp8 depends on phase separation. Additionally, whether our studies on phase separation of nsp8 can facilitate developing a potential strategy against SARS-CoV-2 remains to be further explored.SARS-CoV-2 replication machinery could be concentrated into droplets of N-RNA, suggesting N protein may modulate SARS-CoV-2 replication via LLPS24. Thus, our results on the phase separation behavior of non-structure protein nsp8 will bring a better understanding of the primer synthesis mechanism and amplified replication of coronavirus, not limited to SARS-CoV-2.The transcription and replication machinery is highly conserved across coronavirus, including Alpha-, Beta-, Gamma-, and Delta-coronaviruscDNA fragment encoding SARS-CoV-2 nsp7, nsp8, or truncated nsp8 mutant was cloned into pET21a with C-terminal 8 x His-tag. Plasmids of pET21a-nsp7 and pET21a-nsp8 were provided by Dr. H. Eric Xu. Proteins were expressed in E. coli strain BL21 (DE3) at 16 °C overnight and induced with 0.1 mM IPTG. Bacteria were collected and re-suspended in lysis buffer . Cells were lysed by high-pressure homogenization. After centrifugation, the supernatant was loaded onto Ni-NTA resin. After washing with 70 mM imidazole, the bounded protein was eluted with 300 mM imidazole. Proteins were further purified by gel filtration using a HiLoad Superdex 200 (for wild-type nsp8) or 75 (for nsp7 or truncated nsp8) equilibrated against a buffer of 20 mM HEPES pH 7.4, 0.5 M NaCl, 1 mM DTT.Primers for nsp8ΔN76 mutation: CTTTAAGAAGGAGATATACATATGGAAGATAAACGTGCAAAAG (Forward), and CTTTTGCACGTTTATCTTCCATATGTATA TCTCCTTCTTAAAG (Reverse).Small-angle neutron scattering measurements were performed at the China spallation neutron source (CSNS). 10 mg/mL nsp8 samples in D2O buffer were measured in quartz cells with a 2 mm optical path length. The scattering experiment times of buffers and samples are 90 min. And the empty beam and empty quartz cell were measured for 15 min for data reduction. Transmissions were measured for 10 min for each sample and the empty beam. All measurements were carried out at 289 K.25 provided by the instrument. The neutron scattering intensity has been calibrated to absolute intensity using a secondary standard, Bates poly. The SasView (https://www.sasview.org/) software was employed to calculate the radius of gyration Rg of all samples by Guinier approximation.The presented SANS data were reduced and corrected for sample transmission, cell scattering, and detector background using reduction algorithms developed based on the Mantid frameworkNsp8 dimer or tetramer with a concentration of 1 mg/mL was dissolved in a buffer containing 20 mM HEPES pH 7.4, and 125 mM or 250 mM or 500 mM NaCl. The DLS analyses were performed using DynaPro NanoStarTM (Wyatt Technologies) at 25 °C. The quartz cells were filled with 100 μL 1 mg/mL nsp8 sample solution for DLS measurement. The samples were measured through a 100 mW He-Ne laser, λ0 = 660 nm, θ = 90°. Ten times successive measurements were performed per sample for average to get the DLS data. The DYNAMICS software (Wyatt Technologies) was employed to analyze the DLS data to get the RH of all samples. Data from three independent experiments were used for analysis.SARS-CoV-2 nsp8 dimer and tetramer with concentration 1 mg/mL were dissolved in 20 mM HEPES pH 7.4, and 150 mM or 250 mM or 500 mM NaCl. Thermo-stability analysis was performed with Prometheus NT.48 instrument (Nano Temper Technologies). The scan temperature was increased linearly with a rate of 1 °C per min. Melting temperature (Tm) and onset temperature of aggregation (Tagg) were analyzed with PR.ThermControl software. Data from three independent experiments were used for analysis.For differential interference contrast (DIC) imaging of SARS-CoV-2 nsp8, the proteins were spotted on a glass slide. Imaging was performed with Leica DM4 B.For fluorescence imaging of SARS-CoV-2 nsp8, the proteins were labeled with a His-tag labeling kit by mixing protein and RED-tris-NTA 2nd Generation dye with a mole ratio of about 600:1 and incubating at room temperature for 15 min. To investigate the effect of RNA on phase separation of SARS-CoV-2 nsp8 tetramer, RNA was added to the RED-tris-NTA labeled protein. ssRNA sequences used in this study, R12: rGrArGrArArUrGrArCrArArA, U12: rUrUrUrUrUrUrUrUrUrUrUrU, U22: rUrUrUrUrUrUrUrUrUrUrUrUrUrUrUrUrUrUrUrUrUrU, hairpin RNA: rUrUrUrU rCrArUrGrCrUrArCrGrCrGrUrArGrUrUrUrUrCrUrArCrGrCrG. For co-localization experiments, R12 was labeled with 6-FAM at 5′ terminal. Before imaging, 10 μL aliquot of sample was placed into a well of glass bottom 384 well plates. Imaging was performed with Zeiss LSM 800 Confocal Laser Scanning Microscopy.Nsp8 dimer phase separation was induced using 1 mg/mL RED-tris-NTA labeled nsp8 dimer in buffer containing 20 mM HEPES pH 7.4, and 50 mM NaCl. FRAP experiments were recorded on a Leica SP8 X STED Laser Scanning Confocal Microscopy using a 100x objective and a 650 nm laser. The region of interest loop (ROI) was bleached with a laser power of 30% and an exposure time of 30 ms. Recovery was imaged at low laser intensity with a power of 10%. Five images and 100 images were obtained before bleach and post-bleach, respectively, with one frame 2 s. Data from three independent experiments were used for analysis.5 on a glass bottom culture dish (28.2 mm), and cultured in DMEM medium supplemented with 10% FBS. Plasmid pCDNA3.1-nsp8-mCherry (2 μg) was transfected into Hela cells with Lipofectamine 2000 following the manufacturer’s instructions.For expressing in Hela cells, nsp8 was fused with mCherry-tag at the C-terminal, and cloned into vector pCDNA3.1. Hela cells (ATCC HTB-22) were seeded at 2 × 10nsp8-mCherry and lentiviral packaging plasmids (psPAX2 and pMD2.G) were co-transfected into HEK293T cells (ATCC CRL-1573) in six holes’ plate. After 48 h incubation, the virus supernatant was collected by centrifugation at 500 × g for 5 min. For lentiviral infection, BEAS-2B cells (ATCC CRL-9609) were plated with polybrene (8 ug/mL) and 150 uL prepared lentivirus. After 48 h incubation, BEAS-2B cells stably expressing nsp8-mCherry were selected based on antibiotic resistance using 2 μg/mL puromycin. For imaging, BEAS-2B cells stably expressing nsp8-mChery were seeded at 2 × 105 on a glass bottom culture dish (28.2 mm), and cultured in DMEM medium supplemented with 10% FBS.To prepare nsp8-mCherry stably expressing BEAS-2B cell line, lentivirus vector of pRlenti containing Before imaging, the culture medium was replaced with phenol red-free DMEM supplemented with 10% FBS. Imaging was performed with Laser Scanning Confocal Microscopy Leica SP8 X STED.Primers for pCDNA3.1-nsp8-mCherryForward: CCAGCTAGCATGGCCATTGCCAGCGReverse: TGGCTCGAGCTGCAGTTTCACTGCACPrimers for pRlenti-nsp8-mCherryForward: CGCGGATCCATGGCCATTGCCAGCGAATTTTCReverse: CCGGAATTCCTTGTACAGCTCGTCCATGCDynamic light scattering, thermo-stability analysis, and FRAP experiments were repeated three times. Statistical analyses were performed using GraphPad Prism 7 software.Further information on research design is available in the Peer Review FileSupplementary MaterialDescription of all Supplementary DataSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting summary


The combination of histological and biomolecular analyses provides deep understanding of different biological processes and is of high interest for basic and applied research. However, the available analytical methods are still limited, especially when considering bone samples. This study compared different fixation media to identify a sufficient analytical method for the combination of histological, immuno-histological and biomolecular analyses of the same fixed, processed and paraffin embedded bone sample. Bone core biopsies of rats’ femurs were fixed in different media , methacarn (MFPE) or formaldehyde (FFPE)) for 1 week prior to decalcification by EDTA and further histological processing and paraffin embedding. Snap freezing  and incubation in RNAlater were used as additional controls. After gaining the paraffin sections for histological and immunohistological analysis, the samples were deparaffined and RNA was isolated by a modified TRIZOL protocol. Subsequently, gene expression was evaluated using RT-qPCR. Comparable histo-morphological and immuno-histological results were evident in all paraffin embedded samples of MFPE, FFPE and R + FFPE. The isolated RNA in the group of MFPE showed a high concentration and high purity, which was comparable to the UFT and RNAlater groups. However, in the groups of FFPE and R + FFPE, the RNA quality and quantity were statistically significantly lower when compared to MFPE, UFT and RNAlater. RT-qPCR results showed a comparable outcome in the group of MFPE and UFT, whereas the groups of FFPE and R + FFPE did not result in a correctly amplified gene product. Sample fixation by means of methacarn is of high interest for clinical samples to allow a combination of histological, immunohistological and biomolecular analysis. The implementation of such evaluation method in clinical research may allow a deeper understanding of the processes of bone formation and regeneration. Bone regeneration is a complex process in which different cell types, signaling molecules and extracellular matrix proteins are involved .The regeneration of bone defects resulting from different diseases, cancer or trauma is often a challenging clinical indication ,3,4. DeeBasic research studies that investigate bone tissue are mainly performed in preclinical models with standardized design and under controlled conditions ,6,7. ThiTo translate basic research findings to clinical applications, clinical studies are mandatory. In this case, the amount and size of samples gained from patients are very limited. Additionally, the transport of clinical samples from the operation theater to the analyzing lab is often time consuming and requires high logistic efforts. Therefore, the evaluation of samples gained from clinical studies is often limited to clinical observational outcomes , radioloCurrently, the gold standard to analyze bone tissue samples from clinical studies is histological analysis ,14,15. TBiomolecular analysis of the gene expression is a further method that may elucidate many yet unanswered questions. However, the combination of histological analysis and biomolecular evaluation of clinical samples, especially bone biopsies, is still a very challenging task. One of the most limiting parameters is the number and size of the clinical biopsies that can be gained after bone treatment, for example, prior to the insertion of dental implants . In thisMany efforts have been invested in establishing sufficient methods for nucleic acid isolation form bone samples . IntroduAn additional challenge when analyzing bone samples is the need for decalcification prior to histological analysis. This step may also impair the yield RNA quality . CurrentTherefore, the aim of the present study was to evaluate different analysis protocols to allow a combined histological, immuno-histological and biomolecular analysis of the same fixed, processed and paraffin embedded bone sample. For this purpose, standardized bone biopsies were treated with different fixation media and evaluated using both histological and biomolecular methods.n = 8) were sacrificed with an overdose of ketamine/xylazine from another preclinical study, which was already approved by the responsible regulating authorities of Darmstadt, Germany (FK1023 Regierungspräsidium Darmstadt). The femurs that resulted as waste were used for the present study. All used animals were healthy adult rats that were used as a control group and were hold under the same condition without further manipulation. Per animal, five bone samples 2 mm deep were taken from the distal femoral metaphysis using a 3.2 mm diameter trephine drill, resulting in a sample size of 2 × 3 mm (14 mm3).Eight adult Wistar rats (One of the samples was randomly assigned to the control group of unfixed frozen tissue (UFT) and the other four samples were treated following different fixation and preparation protocols as described in Bone samples were prepared for histological analysis and embedded in paraffin (PE). For demineralization, each sample was incubated in 1 mL of EDTA solution (RNase free) for three days at 4 °C. The samples were then processed manually at room temperature (RT) under RNase free conditions, as follows: 70% ethanol for 45 min, 96% ethanol for 105 min (one change), 100% ethanol for 165 min (two changes), xylene for 135 min (two changes). Finally, the samples were immersed in paraffin wax for 165 min (one change) at 58 °C and embedded in paraffin blocks. The paraffin blocks were stored at 4 °C until RNA extraction.® Plus, Menzel Gläser, Thermo scientific, Dreieich, Germany). Prior to staining, the sections were first deparaffinized in xylene and then rehydrated in a descending ethanol series as previously described [For the histological analysis, five sections of 4-µm thickness were cut from each paraffin block using a rotary microtome . The sections were attached to positive charged glass slides for better adhesion , and section images were photographed with a microscope digital camera . The samples were blinded and evaluated by two of the authors (SA and PV) individually. Staining characteristics, tissue architecture, cell morphology and sharpness of the outlines were evaluated on a three-point rating scale ranging from 1 (not preserved), 2 (impaired) to 3 (preserved). The sectional images of the FFPE bone specimens were considered as standard control and thus used as reference.For the unfixed frozen bone samples from the control group UFT, a modified TRIZOL protocol for RNA isolation was performed immediately after sample collection.®  previously precooled on ice, and mixed on a vortex for 10 s. Following the protocol of Cepollaro et. al. 2018 [All bone samples were first incubated in liquid nitrogen for 10–15 min before being rapidly mechanically crushed with a hammer in an envelope made of sterile foil. The resulting fragments were placed in 1 mL of TRI Reagental. 2018  to aid hIn case of the one-week fixed bone samples , the following pre-treatments were performed prior to RNA isolation. For the PE test groups, the remaining bone samples were removed from the paraffin blocks using a 4 mm diameter tissue punch & excess paraffin was carefully removed. Deparaffinization of the samples was performed as described in The spectrophotometer NanoDrop 2.000  was used to measure the RNA concentration and contamination level in 1 µL of each RNA solution. The solutions were considered pure if the 260/280 nm and the 260/230 nm ratio were greater than 1.8 ,30.TM Classic RNA/DNA stain . The gel was analysed using the ChemiDoc XRS + imaging system and Image Lab software .To evaluate ribosomal RNA (rRNA) integrity, the isolated RNA was electro-phoretically separated to assess the ratio of the 28S to the 18S band of the rRNA subunits. Immediately after isolation of RNA, gel electrophoresis was performed using 300 ng of total RNA per sample. As a positive control, an appropriate amount of osteoblast RNA previously isolated from a human osteoblast culture was run along with each gel. All RNA solutions were prepared to a total volume of 18 µL with nuclease-free water and 3 µL of loading buffer and incubated at 70 °C for 10 min to break potential secondary structures. The RNA samples were separated onto a 1% agarose gel containing TAE buffer (1X) and SafeView® Green JumpStart™ Taq ReadyMix™ , 0.9 µL each of forward and reverse primers (10 µM), 4.2 µL of aqua ad injectabilia and 4 µL of the respective cDNA solution (diluted to 1 ng/µL) or aqua ad injectabilia (NTC). The experiments were performed in triplicates. Initial heat activation  was followed by 40 cycles of denaturation , annealing and elongation .The yield RNA was reverse-transcribed to cDNA using Omniscript RT Kit-Master Mix  and randomized hexamer primers  according to the manufacturer’s instructions. For quantitative real-time polymerase chain reaction (qPCR), primers were designed as single-stranded 25 nmole DNA oligonucleotides based on the U.S. National Center for Biotechnology Information (NCBI) nucleotide database and obtained from Integrated DNA Technologies  . TherebyThe ΔCt method was calculated in relation to the reference genes for each sample:For visualization, after the qPCR run 10 µL of each qPCR reaction solution was diluted with 5 µL aqua ad injectabilia and 3 µL DNA Dye Nontox  and applied to a 2.5% agarose gel containing TAE buffer (1X).n = 8 samples per group). The arithmetic mean (ø) and standard deviation (±SD) were used to evaluate the statistical significance by a simple analysis of variance (ANOVA) followed by Tukey’s test, performed using the program GraphPad Prism version 8.0.1 . The statistical significance level was set at p < 0.05 and marked with (*). Differences with a p value < 0.01 were considered very significant (**) and with p < 0.001 highly significant (***). The graphical representation of the results was in the form of a bar or box-whisker plot, also by the GraphPad Prism program. The cross bar in the middle of the box indicates the median.Statistical analysis was performed on the data obtained from NanoDrop analysis and Ct values from the qPCR of all five groups . However, in the group of R + FFPE a slight loss of quality was observed, especially when evaluating the margins of single structures, that appeared rather blurred and were less well preserved compared to the other groups. The immunohistochemical staining showed a positive and structure-specific staining in all groups. CD 68 positive cells were exhibited all over the samples. These cells are considered a special type of macrophage residing in the bone tissue , Figurea–l.Biomolecular evaluation included the RNA quantity and quality as well as the gene expression as described below.p < 0.001 for both), , a.p < 0.01 for FFPE and *** p < 0.001 for R + FFPE), (p < 0.0001 for both), , b. Moreovr both), c.However, the evaluation of the ribosomal RNA (rRNA) integrity using gel electrophoresis showed two clearly distinguishable bands with a 1:2 ratio for the unfixed groups UFT and RNAlater that were comparable to the positive control (osteoblasts), but not in the MFPE group. No gel electrophoresis could be performed for the other two fixed groups FFPE and R + FFPE, due to the lack of RNA quantity extracted d.Gel electrophoresis was performed after qPCR to assess whether the respective gene product was amplified correctly. In this case, the correct gene product was adequately localized on the agarose gel in the groups of RNAlater, MFPE and UFT, especially for the genes B2M and Col1a1, whereas the gene products of these two evaluated genes were not identifiable in the groups of FFPE and R + FFPE. In the case of PPIB, the bands on the gel were sharply identifiable in the groups of RNAlater, MFPE and UFT. However, the bands in the groups of FFPE and R + FFPE were adequately localized but not sharply distinguishable a.The evaluation of the gene expression was performed using the Ct-values and the ΔCt-values. The Ct-values for each analysed gene (reference and target genes) was compared between the evaluated examples. Interestingly, for all three evaluated genes the Ct-values were the lowest in the group of RNAlater, followed by the group of MFPE and UFT. By contrast, the groups R + FFPE and FFPE showed the highest Ct-values b.p < 0.001 for both), , c.The use of different analysis methods to evaluate bone formation and regeneration allows a deeper understanding of pathophysiological mechanisms and evaluation of treatment techniques ,32. ThusIn the present study, cylinder core bone biopsies with 3 mm diameter and 2 mm length were obtained from the femur of Wistar rats as a model for core biopsies to represent the clinical procedure applied in the field of oral and maxillofacial surgery .The histological analysis of the fixed and processed samples documented by H & E staining showed adequate results concerning the staining quality and structure preservation in both groups, MFPE and R + FFPE, compared to the gold standard FFPE, although a slight loss of quality in the sharpness of the structures was observed in the group of R + FFPE . AdditioThe results of the biomolecular analysis achieved a relatively high concentration of RNA gained from the MFPE group that was comparable to the unfixed groups of UFT and RNAlater. However, it was significantly higher compared to the other two fixed and paraffin embedded groups of FFPE and R + FFPE. These outcomes are similar to previously reported results in the literature evaluating unmineralized tissue fixed using methacarn and showing comparable results to those of snap freezing ,37. HoweIn addition to the RNA quantity, the quality of the RNA yield plays an important role for the subsequent biomolecular analysis. Our results showed a very good RNA quality in the MFPE group (1.99 and 1.89) that was comparable to UFT and RNAlater, whereas the FFPE and R + FFPE showed a significantly lower quality when considering the 260/280 and 260/230 ratio. Similar results showing such high purity were not found in the literature at this time point. However, previous studies reported a sufficient RNA quality with ratios ranging from 1.0 to 1.32, when soft tissue samples were fixed using ,36. The The integrity of the isolated RNA was the highest in the groups of UFT and RNAlater that did not undergo decalcification or paraffin embedding. In comparison, the group of MFPE did not show a defined 2:1 ratio of the 28S/18S bands as documented by gel electrophoresis. No comparison with the groups of FFPE and R + FFPE is possible because these groups did not provide enough RNA to perform gel electrophoresis. These results showed that, despite the high amount of the isolated RNA from the MFPE group and the high purity, the RNA integrity was affected by the fixation, decalcification, processing and paraffin embedding. Similar results were also reported in previous studies showing the lack of RNA integrity in the methacarn fixed and paraffin embedded samples ,38. RNA In this context, in our study the isolated total RNA of all groups was used for RT-qPCR regardless of the results of the gel electrophoresis (rRNA integrity). Gel electrophoresis of the amplified genes showed clearly and identifiable gene product that was only correctly amplified in the groups of RNAlater, UFT and MFPE. Similar results were documented by previous studies showing an affected RNA integrity but positive RT-qPCR results, especially when using methacarn as a fixative medium ,38. In cBased on the described findings, the base pair length of the addressed target gene became of high importance when using compromised RNA. Thereby, the integrity of the isolated RNA has to be sufficient to amplify the targeted qPCR product. In our case, a rather small amplicon (Col1a1) with a base pair length of 192 bp was targeted. Other studies showed successful amplification of similar gene products such as BMP-2 (69 bp) or Runx (395 bp) (,38 afterInterestingly, the RT-qPCR results showed similar Ct-values in the groups of UFT, RNAlater and MFPE. In contrast, the groups of FFPE and R + FFPE showed higher Ct-Values, which are not comparable with the other groups, because the target gene was not amplified correctly. These results are consistent with the quantity and quality of the isolated RNA as documented by the spectrophotometric analysis. Consequently, the calculated ΔCt-values were comparable in the groups of MFPE and UFT when analyzing the expression of Col1a1. In the present study the calculated ΔCt-values were used instead of the calculated ΔΔCt-values, because no treatment was performed that was expected to influence the RNA expression. Therefore, no treatment control is available to calculate ΔΔCt-values. Based on the addressed question and the used native bone samples ΔCt-values were found to be sufficient to evaluate the gene expression at this stage. Further studies using native and treated bone may be necessary to validate the presented results.The demonstrated results may be explained by the known mechanisms of tissue fixation using formaldehyde in comparison to other fixatives. Formaldehyde leads to crosslinking of the nucleic acids during two reactions including an addition of N-methylol and an electrophilic attack to form a methylene bridge between two amino groups. Specifically the adenine base is affected by this reaction leading to a heavily modified poly-A tail of the treated mRNA ,44. ConsAltogether, the results of the present study introduced a possibility of performing combined histological, immunohistological and biomolecular analysis using the same decalcified and paraffin embedded bone sample. The RNA isolated by using methacarn fixation followed by EDTA decalcification was shown to be sufficient for analysis and to meet the most important clinical requirements. The suggested protocol allows its implementation in the clinical routine to enable multidisciplinary analysis from valuable clinical samples of limited size. One possibility for the implementation of such a method may be the analysis of samples resulting from treatment with bone substitute materials or tissue engineering scaffolds. This may enhance the multidisciplinary analyses of clinical samples and gain a deeper understanding of bone mechanisms, for example, in the field of biomaterials-based bone regeneration and tissue engineering.The present study evaluated different fixation media to identify an effective analysis protocol that enables the histological, immunohistological and biomolecular analysis of the same fixed, decalcified, processed and paraffin embedded bone sample. The results showed that a one-week fixation using methacarn, followed by EDTA decalcification for 3 days, allows comparable histological and immunohistological results to the formaldehyde fixed samples, and enables RNA isolation with a high quantity and quality that is comparable to snap frozen samples. This method is promising for implementation in clinical studies with limited sample size.


Our data revealed a pattern of restricted gene flow and limited genetic connectivity among geographically distant reef systems. Environmental association analyses identified a suite of loci strongly associated with the regional temperature variation. These loci helped forecast future genetic offset in gradient forest and generalized dissimilarity models. These analyses predicted pronounced differences in the response of different reef systems in Western Australia to rising temperatures. Under the most optimistic future warming scenario (RCP 2.6), we predicted a general pattern of increasing genetic offset with latitude. Under the extreme climate scenario (RCP 8.5 in 2090–2100), coral populations at the Ningaloo World Heritage Area were predicted to experience a higher mismatch between current allele frequencies and those required to cope with local environmental change, compared to populations in the inshore Kimberley region. The study suggests complex and spatially heterogeneous patterns of climate‐change vulnerability in coral populations across Western Australia, reinforcing the notion that regionally tailored conservation efforts will be most effective at managing coral reef resilience into the future.Anthropogenic climate change has caused widespread loss of species biodiversity and ecosystem productivity across the globe, particularly on tropical coral reefs. Predicting the future vulnerability of reef‐building corals, the foundation species of coral reef ecosystems, is crucial for cost‐effective conservation planning in the Anthropocene. In this study, we combine regional population genetic connectivity and seascape analyses to explore patterns of  First, we used the GBS approach to measure levels of spatial genetic structure across Western Australia to infer levels of reproductive isolation within and among separate geographic populations. Secondly, we utilized gene–environment association analyses to identify putatively adaptive variants likely to be under directional selection. Finally, we used these loci to predict mismatches in GEAs under future climate scenarios.In Western Australia, large‐scale population connectivity studies have combined genotype data with environmental variables into integrated seascape analyses  were collected from 31 sites across the four aforementioned reef systems in Western Australia  The TheA. dia Figure , along wa Figure . Samplesa Figure . FurtherSymbiodinidae sequences with an e‐value below the threshold and were discarded for downstream filtering  as outliers, probably representing a cryptic species Tables . These sng Table . Two genng Table  was filtng Table , averageng Table . Further2.2FST and genetic differentiation between reef systems, reefs and sites  under future climate conditions , which is an adapted Bayesian approach that combines Environmental variables were selected based on their importance in delineating coral growth, settlement and survival Table  Maina e and can 2 values. For the GDM, the default model setting of three I‐splines was used. GDM performance was assessed based on % deviance explained and the relative variable importance was represented by the sum of I‐spline coefficients , CCSM4, HADGEM2‐ES and MIROC 5, from the CMIP 5 database   and were run through BayeScan 2.0. Overall, 1193 loci, located inside a 95% false discovery rate threshold, were considered putatively neutral loci. This neutral loci dataset was used to explore population connectivity among reef systems in Western Australia and the GBR. Across the five reef systems (including the GBR), the overall expected heterozygosity was 0.365 and the mean 06 Table . Pairwisls Table  and S8, 9) Table . At the 9) Table . On the 9) Table ; one clu9) Table . When th81 Table  (mean F3.2r = −0.83), which was retained because of the availability of future SSTrange data. Using BayeScEnv, we identified 110 significant gene–environmental associations across 65 unique and polymorphic loci (>20% of the sampled sites), potentially under environmental selection across our study domain, that were integrated into the GF and GDM algorithms. Of these 110 significant associations, the majority were strongly correlated to SSTmax, tidal height and SSTrange conditions across the WA reef sites .Thirteen environmental variables were initially considered in our analyses Table . Nine va2 = 0.457 ± 0.253). These loci were used to extrapolate the gene–environmental associations to a broader spatial scale and to examine how these associations change under future climate conditions. Variable importance for the best performing GF model was highest for tidal height, followed by SSTmax, SSTrange, bathymetry and SSTA  across all climate scenarios  compared to RCP 8.5 in 2040–2050 and RCP 2.6 in 2040–2050 and 2090–2100 Figure . Two pris Figure , except s Figure . The GDMs Figure . These ps Figure  and 4 whs Figure .4Acropora digitifera across 12 degrees of latitude, we identified strong population differentiation among geographically separated reef systems, indicating restricted connectivity and limited gene flow between inshore and offshore reef systems in Western Australia (WA). Loci showing strong associations with temperature revealed varying genetic offsets among different reef systems. Based on the model results presented in this study, corals living closest to their thermal stress limit in low latitude regions, such as the inshore Kimberley reef system, were predicted to require a lower adaptive shift to be able to cope with future increases in temperature, compared to mid‐latitude reefs. For example, populations in the Ningaloo Coast World Heritage Area were predicted to have pronounced gene‐environment mismatches under future climate scenarios, highlighting their vulnerability to forecasted temperature changes and the need for large and rapid adaptive shifts to keep pace with climate change. This study shows that the potential of coral populations in WA to maintain gene‐environmental associations under climate change is quite variable, complex and highly correlated with the relative regional temperature shifts, projected under climate change conditions. While the predictions of genetic offset in this study are based on future sea surface temperature conditions only, the importance of shifts in other factors such as fine scale future temperature anomalies cannot be ignored.Using a panel of genomic‐wide SNPs to explore the genetic diversity, population structure and mismatch in future gene–environment associations of 4.1Acropora digitifera among, but not within, reef systems in WA with substantial exchange of beneficial alleles within systems. Consistent with the expectations of metapopulation structure, populations from the Great Barrier Reef showed strong genetic divergence from WA samples. Within WA, distinct genetic differences were identified between populations from the offshore reef systems, the inshore macrotidal Kimberley region and Ningaloo Coast World Heritage Area reefs. The spatial patterns of restricted exchange of genetic material between reef systems are similar to that observed in other brooding and spawning coral species in northwest Australia  and revealed different degrees of genetic offset across the reef systems in WA. For example, A. digitifera populations are adapted to their local unique environmental conditions  is inversely related to the predicted genetic offset. This means that strong adaptation to local temperature conditions could result in lower future mismatches in gene‐environmental associations and potentially increased resilience potential. Tidal height, SSTrange and SSTmax were identified as the strongest drivers of local adaptation and could be considered key environmental variables across all reef systems in tropical WA, although their influence diminishes from low to mid latitude reef systems . These results reflect the variety of unique environmental conditions documented in the studied reef systems , assuming no future changes in migration, reproductive success, brood stock, mutation rate and local adaptation potential, or shifts in anomalous conditions or population dynamics. All of these factors are considered to potentially influence coral reef resilience under climate change conditions but are difficult to project over time. Many studies have discussed the impact of extensive heat stress (Zinke et al., A second potential limitation in this study, is that the selection of outlier loci was based on statistical analyses in BayeScEnv, rather than a priori knowledge of adaptive SNPs as seen in Fitzpatrick and Keller . HoweverA. digitifera are transferable to other coral species with similar or different reproductive modes is unknown. In contrast to broadcast spawning corals that release gametes in the water column that can travel over large distances, brooding corals release larvae in close proximity to the parents, which makes the latter particularly more vulnerable to changing climate conditions. Based on our findings, we hypothesise that brooding coral populations, which are highly adapted to local conditions, could experience even higher mismatches in gene‐environmental associations with the increasing rate of future temperature shifts.Whether the broadscale projects of gene‐environmental mismatches that we described here for Based on these projections, we can assume that coral populations at tropical reef systems in WA, which predominantly depend on local recruitment to replenish populations after disturbance events, will respond differently to climate change pressure. As the potential for populations to adapt to climate change conditions is strongly correlated with the magnitude in temporal temperature shifts, populations such as those in the inshore Kimberley showed to experience the lowest mismatch in genetic variation under future temperature shifts. While inshore Kimberley populations are predicted to experience the lowest genetic offset across reef systems in WA, it is uncertain whether these populations have the capacity to respond and adapt fast enough to keep up with increasing frequency and magnitude of temperature change. Therefore, the gene‐environmental associations analyses in this study provide the building blocks for future research to investigate rates of adaption and whether the shifts in population genetics are likely to convey greater resistance of coral reef systems to future heat stress. Nevertheless, the increased pressure of climate change, variability in environmental responses as well as spatial and genetic isolation of coral populations in WA, calls for regionally tailored conservation and management strategies to monitor how the metapopulation responds to the increased intensity of climate disturbances in the future.5This study identified an increasing vulnerability of coral populations in Western Australia to rising global temperatures. It also supports the notion that reef systems in WA are highly adapted to local environmental conditions, reproductively isolated from neighbouring systems, and therefore self‐reliant for population maintenance and genetic rescue. However, our data also revealed pronounced differences in genetic offset among our sampled reefs, offering a glimmer of hope that some reef systems, such as the inshore Kimberley, may fare better than others under climate change conditions. However, inferences about future adaptive potential for populations are based on the observed distribution of heat adapted alleles, which are strongly correlated with the background exposure to higher temperatures. Furthermore, our results show that the capacity of populations to maintain present‐day adaptive potential under climate change conditions is highly dependent on the magnitude of regional temperature change predicted in the future. Nonetheless, the primary factor determining the impact of climate change on coral reefs is the frequency and severity of temperature increases, which typically overwhelm the latent adaptive capacity of many reefs and habitats. Variation in adaptive capacity will slow the degradation of some populations on some reefs; however, reducing rates of temperature increase generated through carbon emissions remains the most effective means maintaining coral reef ecosystems into the future. Given the prediction of recurrent mass mortality events in the future, broadly evaluating the metapopulation structure and the adaptive capacity of populations provides useful information for the prioritization of limited conservation resources.Arne Adam, Luke Thomas, Jim Underwood and Zoe Richards conceived the study; Zoe Richards and James Gilmour secured project funding and arranged fieldwork logistics. James Gilmour and Luke Thomas provided data from the Rowley Shoals and Ashmore Reef. Zoe Richards and Arne Adam collected additional samples from the inshore Kimberley and Ningaloo World Heritage Area; Arne Adam conducted the data analyses. Luke Thomas, Jim Underwood, James Gilmour and Zoe Richards advised on data analyses; Arne Adam wrote the manuscript; All authors contributed to manuscript editing.All authors declare no conflict of interest.Data S1Click here for additional data file.