The kinetic and structural analyses show that K30D has an accelerated biphasic transition from I2 to I3 by more than nine times compared to WT and lacks significant structural changes in the transition from R-like I2 to T-like I3 observed in WT, unveiling that the loss of the salt bridges interrupts the R–T allosteric transition of HbI. Besides, the correlation between the bimolecular CO recombination rates in K30D, WT, and other mutants reveals that the bimolecular CO recombination is abnormally decelerated in K30D, indicating that the salt bridges also affect the cooperative ligand binding in HbI. These comparisons of the structural dynamics and kinetics of K30D and WT show that the interfacial salt bridges not only assist the physical connection of two subunits but also play a critical role in the global structural signal transduction of one subunit to the other subunit via a series of well-organized structural transitions.A salt bridge, one of the representative structural factors established by non-covalent interactions, plays a crucial role in stabilizing the structure and regulating the protein function, but its role in dynamic processes has been elusive. Here, to scrutinize the structural and functional roles of the salt bridge in the process of performing the protein function, we investigated the effects of salt bridges on the allosteric structural transition of homodimeric hemoglobin (HbI) by applying time-resolved X-ray solution scattering (TRXSS) to the K30D mutant, in which the interfacial salt bridges of the wild type (WT) are abolished. The TRXSS data of K30D are consistent with the kinetic model that requires one monomer intermediate in addition to three structurally distinct dimer intermediates (I The structural and functional roles of salt bridges in the allosteric structural transition of homodimeric hemoglobin are investigated by time-resolved X-ray solution scattering. Many cases have been reported where the proteins cannot perform their original functions when the salt bridges are abolished.2–4 Although much research has been conducted on salt bridges due to their importance in determining the structures and functions, most studies have been conducted only on static equilibrium states. In reality, however, performing a protein function is not a static process in which the protein stays in a specific structure but a dynamic process in which the structure changes over time. Therefore, it is important to identify how the salt bridge affects the structural dynamics of a protein, for example, the structures of reaction intermediates and the associated reaction rates.A function of a protein has an intimate connection with its three-dimensional structure. Thus, understanding the interactions inside the protein that affect the structure provides key clues for uncovering the function of a biological system. The structure of a protein is determined by the covalent interactions between amino acid residues to establish the backbone of the protein as well as the non-covalent interactions such as hydrogen bonds, van der Waals interactions, ionic interactions, and hydrophobic interactions that determine how the protein folds. The salt bridge is one of the typical structural factors generated by non-covalent interactions found in many proteins and is formed by a combination of hydrogen bonds and ionic interactions. In a protein, a salt bridge is mainly constructed between a negatively charged carboxylate of aspartic acid or glutamic acid and cationic ammonium of lysine or guanidinium. Many studies have been conducted to unveil the structural and functional roles of salt bridges in proteins. The salt bridge in the structural aspect mainly plays a role in stabilizing entropically unfavorable conformations, enhancing the stabilities of secondary, tertiary, and quaternary structures, and having resistance to protein aggregation.Scapharca inaequivalvis. Allosteric regulation is one of the essential mechanisms regulating the function of proteins, in which the structural change at one site leads to significant feedback at a remote site of the same macromolecular entity. A well-known example is human tetrameric hemoglobin (Hb), where the binding of dioxygen or carbon monoxide is known to benefit from allosteric regulation.5–12 While the tetrameric structure of Hb makes detailed experimental studies difficult,13 HbI offers a greatly simplified model system, thanks to its homodimeric structure. Despite being only a dimer, the characteristics of cooperative ligand binding and allosteric structural transitions are clearly expressed between the liganded R state with high ligand affinity and the unliganded T state with low ligand affinity.14–26In this regard, here we investigated how the structural dynamics of a protein are altered in the presence or absence of salt bridges to improve the understanding of the structural and functional role of the salt bridges in the course of the dynamic biological process. Specifically, we investigated the role of the salt bridges during the allosteric structural transition of homodimeric hemoglobin (HbI) from HbI has two symmetrically linked salt bridges between Lys30 and Asp89 at the subunit interface. The salt bridges are one of the essential factors composing the intersubunit network and stabilizing the dimeric assembly with two subunits connected. By two symmetrically linked intersubunit salt bridges between Lys30 in one subunit and Asp89 in the other subunit, one subunit may communicate to the other subunit. The K30D mutation, where the Lys30 residues of the two subunits consisting of the salt bridges of the wild type (WT) are replaced with Asp30 residues, abolishes the only salt bridges at the subunit interface by introducing charge repulsion between Asp30 and Asp89  so that 27 It was reported that the cleavage of the salt bridges destabilizes the intersubunit network and dimeric assembly of HbI, resulting in a change in the mechanism of cooperativity.27 According to the reported dimerization constant,27 where WT mostly exists as the dimeric form in nature, the fraction of the monomer is not negligible in K30D due to the weak interaction at the interface. The local structural changes near the heme pocket and the kinetics upon oxygen release in K30D were investigated by spectroscopic studies and equilibrium oxygen-binding experiments.27 Still, more detailed kinetic and structural information, such as lifetimes and tertiary and quaternary structures of reaction intermediates, is unclear. In this regard, time-resolved X-ray solution scattering (TRXSS)28–33 can provide complementary dynamic information on the allosteric structural transition of K30D in solution for unveiling the effect of the salt bridges in HbI on the structural dynamics. TRXSS, also known as time-resolved X-ray liquidography (TRXL), is a useful technique that offers complementary advantages over conventional methods, as it allows for easy observation of the structural dynamics of proteins in physiological environments.A study using spectroscopic characterization on K30D reported the dimerization constants of unliganded and liganded derivatives, the oxygen equilibrium measurements, and the kinetics of oxygen release.1, I2, and I3) of K30D have R-like structures, unlike WT involving a transition from R-like I1 and I2 to T-like I3. These observations indicate that the dramatic structural change associated with the R–T transition is remarkably suppressed by losing the salt bridges in K30D. Considering that the bimolecular CO recombination is decelerated in K30D although I3 of K30D has a structure more similar to the ground state compared to that of WT, the loss of the salt bridges may diminish the ligand-binding affinity and eventually make the CO recombination process slow.In this work, to elucidate how the interfacial salt bridges play a role in the allosteric structural transition in HbI, we performed the TRXSS experiment on K30D in solution and extracted the structures of the intermediates and determined kinetic parameters based on the modified kinetic model that considered the monomer fraction. In particular, we obtained the detailed structural parameters of the intermediates of K30D HbI, such as root-mean-square deviations (RMSDs), displacement plots, subunit rotation angles, heme–heme distances, and distances between Cα atoms of the key residues involved in the salt bridges in WT HbI, and quantitatively compared these structural parameters with those of WT HbI. We found that all the three dimer intermediates (IE. coli and purified according to the method34 described for WT. Carbonmonoxy derivatives of K30D solution for the photodissociation reaction were prepared as follows. A 2–4 mM deoxy K30D solution in 100 mM phosphate buffer (pH 7) was prepared in a rubber-topped air-tight vial. The protein concentration was determined from the absorbance at 578 nm using the absorption coefficient of heme-oxygenated derivatives (14.3 mM−1 cm−1). The deoxy K30D was reduced by adding 10 μL of 1 M sodium dithionite solution under a nitrogen atmosphere. The reduced samples were exposed to CO gas for 30 minutes to convert deoxy to CO-bound K30D. The sample solution was prepared just before the X-ray solution scattering measurement. An aliquot of the resulting CO-bound K30D solution was transferred into a quartz capillary with a 1 mm diameter (Hampton Research) and immediately sealed with epoxy to minimize gas exchange while CO gas was purged continuously into the capillary.The K30D mutation was introduced into the native recombinant HbI plasmid using the EZchange™ site-directed mutagenesis kit (Enzynomics) with the following oligonucleotide encoding K30D mutation: 5′-GGTTCGGACAAAGATGGTAACGG-3′ and 5′-CCGTTACCATCTTTGTCCGAACC (Genotech). The K30D mutant (K30D) was over-expressed in 2 at the sample position and finally had an energy density of 1 mJ mm−2. The X-ray pulse peaked at 12 keV with a long wavelength tail of ∼4% and a photon flux of ∼109 photons/pulse. The size of the X-ray beam was adjusted to be 0.09 × 0.07 mm2 at the sample position, which was smaller than the laser spot size, to minimize the timing jitter caused by the spatial error between the laser and X-ray pulses. After a laser pulse passed through the sample to induce the reaction, an X-ray pulse arrived with a time delay, Δt, and time-resolved X-ray scattering data were measured at time delays in the range from 100 ps to 10 ms. The helium cone was utilized to reduce the air scattering by X-ray. Two-dimensional (2D) scattering patterns were collected using an area detector (MarCCD) located at a distance of 185 mm from the sample position. The capillary containing the protein sample was moved along the axis perpendicular to the pulse train of X-ray and the laser and was set to perform repetitive motion in the forward and backward directions to provide a fresh sample for each X-ray and laser pair. The obtained 2D X-ray scattering patterns have centrosymmetry due to the random orientation of the molecules in the solution phase, so that one-dimensional (1D) X-ray scattering curves can be obtained by azimuthally integrating the patterns as a function of the magnitude of the momentum transfer vector, q = (4π/λ)sin(2θ/2) where λ is the X-ray wavelength and 2θ is the scattering angle. The 1D X-ray scattering curves, S, contain information about the X-ray scattering occurring from the solvent pairs, solute pairs, and solvent–solute pairs at the corresponding time delay. The X-ray scattering signal from the solvent molecule was much larger than the signal of our interest resulting from the reaction of the solute molecules. The scattering curve at the negative time delay, −5 μs, containing the signal from the unreacted sample solution was subtracted from the scattering curve for each time delay, and the scattering signal generated by the bulk solvent was removed by this process. As a result, we obtained the time-resolved difference X-ray solution scattering curves, ΔS. The time delays used in the experiments are as follows:The TRXSS experiment of K30D was conducted at the BioCARS beamline 14IDB at the Advanced Photon Source using the experimental setup of a typical pump-probe method. Circularly polarized laser pulses generated from a picosecond laser system were sent to an optical parametric amplifier (TOPAS) to generate output pulses with a center wavelength of 532 nm and a temporal duration of ∼35 ps. The laser light was focused on 0.15 × 0.60 mm−5 μs, 100 ps, 178 ps, 316 ps, 562 ps, 1 ns, 1.78 ns, 3.16 ns, 5.62 ns, 10 ns, 17.8 ns, 31.6 ns, 56.2 ns, 100 ns, 178 ns, 316 ns, 562 ns, 1 μs, 1.78 μs, 3.16 μs, 5.62 μs, 10 μs, 17.8 μs, 31.6 μs, 56.2 μs, 100 μs, 178 μs, 316 μs, 562 μs, 1 ms, 1.78 ms, 3.16 ms, 5.62 ms, and 10 ms.35–38 During the experiment, the temperature was maintained at 25 °C by a stream of cold nitrogen gas (Oxford Cryostream).The measured difference X-ray scattering curves contained the signal from the thermal heating of the solvent. For the data analysis, we removed the thermal heating contribution using a well-established method.S, obtained for time delays (t) from 100 ps to 10 ms are shown in q-axis due to protein structural changes. To extract kinetic information of intermediates and their structures from ΔS, we followed the well-established procedure35–38 which had been applied in previous TRXSS studies on WT and various mutants of HbI, consisting of kinetic analysis using singular value decomposition (SVD) and principal component analysis (PCA). As detailed in the ESI,37 T72V,36 and I114F35 can be explained with the same kinetic framework with only minor variations. This framework has three intermediates, a transition from the first intermediate to the second intermediate (I1 → I2), a biphasic transition from the second intermediate to the third intermediate (I2 → I3), and bimolecular recombination to recover CO-bound HbI(CO)2. Compared to the WT data, which exhibit three intermediates and seven time constants, K30D has one additional intermediate and a smaller number of kinetic components, requiring a more significant alteration to the known the kinetic framework.Time-resolved difference X-ray solution scattering curves, Δ39 in addition to the whole data and (ii) SVD-aided pseudo PCA analysis (SAPPA).39 As detailed in the ESI,1 to I2, and the two time constants of 47 ns and 588 ns need to be assigned to the biphasic transition from I2 to I3. As a result, among five time constants, two (616 μs and 5.34 ms) remain to be assigned, and the identity of one intermediate among four remains to be determined.To establish the kinetic framework , we used3 M−1 at 10 °C under the equivalent conditions,27 and the ratio of the monomer is 41% in an aqueous solution. (ii) The monomeric unit of K30D can be regarded as a monomeric heme protein, whose photoinduced structural changes were observed in many time-resolved studies,28,40–55 and thus it is plausible that K30D monomer undergoes the structural change induced by photoexcitation. (iii) The simulated difference scattering intensity of the monomer shows a magnitude that cannot be neglected compared to that of the dimer  and the dimeric form of the initial state as K30D HbI(CO)2. According to the determined kinetic model, K30D HbI(CO)2 is converted to the earliest intermediate, IK30D1, within the experimental time resolution (<100 ps), and IK30D1 transforms to IK30D2 with a time constant of 4.6 ns, which is slightly larger than that of WT (3.2 ns), indicating that the absence of salt bridges affects the earliest intermediates although the effect is not severe. K30D shows the biphasic IK30D2-to-IK30D3 transition, as in WT and other mutants, with time constants of 47 ns and 588 ns, which are smaller than those of WT (730 ns and 5.6 μs) by factors of 15.5 and 9.5, respectively, indicating acceleration with respect to WT. According to TRXSS studies on WT and mutants,35–38 this IWT2-to-IWT3 transition accounts for the quaternary structural change where both the rotation angle and the heme–heme distance significantly changes. Thus, the accelerated rates mean that either (i) these structural changes occur more rapidly or (ii) different structural changes are involved in K30D. This result suggests that the salt bridges between the two subunits may be involved in controlling the quaternary structural change. Then, IK30D3 returns to K30D HbI(CO)2 with a bimolecular rate constant of 36 (±3) mM−1 s−1, which is nearly three times smaller than that of WT (95 mM−1 s−1). For the monomeric form of K30D, initial K30D mHbI(CO) is converted to i, within the experimental time resolution (<100 ps), and returns to K30D mHbI(CO) with a bimolecular rate constant of 309 (±9) mM−1 s−1, which is more than eight times larger than that of IK30D3. The fit result shows 81 (±0.4) % for the fraction of monomers. The reported value at 10 °C is 41%.27 The fact that the TRXSS experiment was carried out at 25 °C can rationalize the higher monomer content. The estimated dimerization constant based on the monomer fractions at 25 °C is ∼77 (±1) M−1. We note that the relative portion of dimers in terms of the concentration that contributes to the difference scattering signal is ∼20% (and that of monomers is ∼80%), but this does not mean that the relative contribution to the difference scattering signal is 20%. One dimer contributes more strongly to the scattering signal than one monomer because a dimer is two times heavier than a monomer and a dimer generally has a larger structural change than a monomer. The relative contributions of dimers to the difference signal range from 25% to 88% depending on time, as shown in Fig. S12.†Finally, species-associated difference scattering curves (SADSs) and time-dependent concentrations of K30D are compared with those of WT in 35–38 which provided direct evidence that the partially photolyzed dimer subunit undergoes the same structural evolution as the fully photolyzed subunit, directly demonstrating the allosteric regulation of HbI.We note that the intermediates of K30D have two substrates of the fully photolyzed and partially photolyzed forms, which are structurally indistinguishable from each other. The same was revealed from previous laser power dependency studies on WT and other mutants,via structural analysis using the SADSs of the intermediates. As shown in 1 and I2 are not much different from each other, suggesting that the structure of I1 and I2 are not affected by the F97Y or T72V mutation. The SADSs of I3 for F97Y and T72V, however, are significantly different compared to WT, indicating that the T72V and F97Y mutations alter only the structure of I3. To extract key structural parameters of each intermediate structure, we performed structure refinement applied with a rigid-body modeling approach using crystallographic structures as template structures, which is established in our previous study on WT.35–38 In the case of K30D, since its crystallographic structures were not reported, we made the template structures for the structural analysis by modifying the crystallographic structures of WT (see the ESIThe information about the 3D structures of the intermediates can be obtained 2 (PDB ID: 3SDH) and unliganded HbI (PDB ID: 4SDH) and for K30D, modified structures based on these; see the ESI† for details), termed RMSDR and RMSDT, respectively, as shown in R and RMSDT values of the intermediate allows for quantifying the degree of resemblance of the R-like structure or the T-like structure. In previous studies, it was found that IWT1 and IWT2 have R-like structures, whereas IWT3 has a T-like structure.35–38 Actually, IWT1 (RMSDR: 0.4 ± 0.05 Å and RMSDT: 0.7 ± 0.05 Å) and IWT2 (RMSDR: 0.4 ± 0.06 Å and RMSDT: 0.7 ± 0.05 Å) have larger RMSDT than RMSDR, whereas IWT3 (RMSDR: 0.7 ± 0.05 Å and RMSDT: 0.4 ± 0.04 Å) has a larger RMSDR compared to RMSDT, confirming that IWT1 and IWT2 have R-like structures and IWT3 has a T-like structure. For K30D, IK30D1 (RMSDR: 0.4 ± 0.05 Å and RMSDT: 0.8 ± 0.06 Å) and IK30D2 (RMSDR: 0.5 ± 0.05 Å and RMSDT: 0.8 ± 0.06 Å) exhibit larger RMSDT than RMSDR, as in WT, indicating that IK30D1 and IK30D2 have R-like structures. However, the opposite trend was observed between IWT3 and IK30D3. IK30D3 (RMSDR: 0.3 ± 0.05 Å and RMSDT: 0.7 ± 0.05 Å) has a larger RMSDT than RMSDR, indicating that IK30D3 has an R-like structure, unlike the T-like IWT3. The fact that all the intermediates of K30D have R-like structures suggests that abolishing the salt bridges in K30D restricts the efficient R–T transition in HbI.The key structural parameters, such as the RMSD, displacement of the residues, heme–heme distance, rotation angle, and for the intermediates. To compare the overall structural differences between the intermediates of WT and K30D HbI, we calculated RMSD values in the positions of the Cα atoms with respect to the reference structures 1 and I2, the magnitudes and directions of the displacements in K30D are similar to those in WT. In the case of I3, however, the magnitudes and directions of the displacements in K30D are different from those in WT. The displacement of IK30D3 is similar to those of IWT1 and IWT2 than that of IWT3, meaning that the tertiary structure of IK30D3 has an R-like structure in contrast to IWT3 having a T-like structure. These trends are also consistent with the results obtained from the comparison of the RMSD values. We note that the displacements and RMSD values of IK30D3 are between those of IK30D2 and K30D HbI(CO)2, suggesting that IK30D3 has a tertiary structure located between IK30D2 and K30D HbI(CO)2 structures. This consideration indicates that, unlike WT, the tertiary structure of IK30D3 returns to that of K30D HbI(CO)2 without undergoing the R–T transition occurring in the IWT2-to-IWT3 transition. IWT3 undergoes a rearrangement process in which the amplitude of displacement changes dramatically, whereas this process is suppressed in IK30D3. These results confirm that the internal helices as well as the interface are affected by the absence of interfacial salt bridges.To identify the detailed tertiary structural changes, we calculated the displacement plots of the three intermediates, as shown in 2 (heme–heme distance = 18.4 Å and subunit rotation angle = 0°) to IWT1 (18.0 ± 0.2 Å and −0.1 ± 0.5°) and IWT2 (17.9 ± 0.3 Å and 0.1 ± 0.5°) involves very small changes in the heme–heme distances and the subunit rotation angles, indicating that IWT1 and IWT2 have the R-like structures. The major quaternary structural changes occur in the IWT2-to-IWT3 transition (16.6 ± 0.2 Å and 3.5 ± 0.6°), indicating that IWT3 has a T-like structure, unlike IWT1 and IWT2. In K30D, the heme–heme distances and subunit rotation angles of both IK30D1 (18.4 ± 0.3 Å and −0.4 ± 0.6°) and IK30D2 (18.6 ± 0.2 Å and 0.5 ± 0.6°) are similar to those of the initial HbI(CO)2 as in IWT1 and IWT2. Besides, the heme–heme distance of IK30D3 (17.8 ± 0.2 Å) is closer to that of HbI(CO)2 than that of unliganded HbI (16.6 Å), unlike IWT3, which has the T-like structure. The subunit rotation angle of IK30D3 (−0.2 ± 0.5°), however, is even closer to that of the initial HbI(CO)2, whereas the subunit rotation angle of IWT3 is close to that of unliganded WT HbI. Therefore, the subunit rotation angle of IK30D3 is much smaller than that of T-like IWT3. The suppression of the structural rearrangement observed in IWT3, such as the contraction of the heme–heme distance and the intersubunit rotation, is consistent with the results obtained by considering the RMSD values and displacement plots. The suppressed quaternary structural changes in K30D may be caused by the weakened inter-subunit interaction of K30D, suggesting that the communication by salt bridges is an essential element for normal allosteric transition in HbI. This restricted R–T transition can account for the acceleration of the IK30D2-to-IK30D3 transition observed in K30D since the transition in K30D does not require significant quaternary structural changes in contrast to WT. Therefore, it takes less time for the I2-to-I3 transition to occur in K30D than in WT.To inspect the detailed quaternary structural changes, we calculated the heme–heme distance and subunit rotation angle of the three intermediates, as shown in r30–89). The 30th and 89th residues originally form the interfacial salt bridges in WT, but the salt bridges are abolished in K30D. We tried to identify how the movements of the helices connected by the salt bridges in WT are affected by the cleavage of the salt bridges in K30D. For this purpose, we compared the r30–89 of all the intermediates of WT and K30D, as shown in K30D1 has a longer r30–89 (8.8 ± 0.2 Å) than IWT1 (8.6 ± 0.3 Å), and also IK30D2 has a longer r30–89 (8.7 ± 0.2 Å) than IWT2 (8.5 ± 0.3 Å). The longer r30–89 distances in K30D than in WT indicate that the two residues are connected less tightly in K30D. The r30–89 values in both K30D and WT become smaller in the I1-to-I2 transition, and the contraction magnitudes (∼0.1 Å) are maintained similarly in both K30D and WT. The r30–89 of IK30D3 (8.7 ± 0.2 Å) is smaller than that of IK30D2, whereas IWT3 (9.1 ± 0.2 Å) has an increased r30–89 compared to IWT2. This trend is in good agreement with that the I2WT-to-IWT3 transition contains a large intersubunit rotation compared to the IK30D2-to-IK30D3 transition. These results indicate that the r30–89 is closely linked to the interfacial salt bridges and the quaternary structural changes such as the subunit rotation angle and suggest that the interfacial salt bridges have a critical role in maintaining the proper distances between helices, and in having the sufficient magnitude of subunit rotation angle in the R–T transition.We also inspected and averaged the distance between Cα atoms of the 30th residue at one subunit interface and 89th residue at the other subunit interface (denoted K30D3 (36.1 mM−1 s−2) is slower than that of IWT3 (95 mM−1 s−2), although IK30D3 has an R-like structure. Intuitively, since IK30D3 has a structure highly are similar to the ground state, the recovery process of IK30D3 is expected to be faster than that of IWT3. In fact, in the case of F97Y and T72V mutants, whose structural dynamics were investigated using the TRXSS technique, the more the quaternary structures of I3 are similar to the ground state, the faster the acceleration of the bimolecular CO recombination process is.36,37 We represent the heme–heme distances and subunit rotation angles of I3 of WT, K30D, F97Y, and T72V in F97Y3 and IT72V3  are faster than that of IWT3. In K30D, however, both the heme–heme contraction and subunit rotation are limited during the R–T transition, but the bimolecular CO recombination of IK30D3 is slower than that of IWT3, unlike in F97Y and T72V. Considering that the bimolecular CO recombination process requires both the structural recovery and ligand binding, the distinct correlation in K30D may indicate that the loss of the salt bridges degrades the cooperative ligand binding affinity of K30D. Conversely, it can be considered that the salt bridges in HbI may play a functional role in ligand binding as well as the structural role.To infer the functional role of the salt bridges in cooperative ligand binding in HbI, we examined the results of the kinetic and structural analyses. Noticeably, the bimolecular CO recombination rate of Ii and the dimer intermediates , respectively. While i has an R-like structure as in the K30D dimers, there are some slight differences in the movement of the CD loop, the N- and C-termini, and the F helix. The CD loop and N- and C- termini are located in the outer regions of HbI and have high flexibility in the solution. Therefore, the flexibility of these regions can lead to different structural changes between the dimer and monomer. The F helix in the monomer is exposed to the solvent, unlike in the dimer where the F helix in one subunit and the other F helix of the other subunit come in contact with each other and form the interface.Fig. S51, I2, and I3) as in WT. But, the kinetic model of K30D involves an additional intermediate (i) for the monomer, of which the fraction dramatically increases compared to WT due to the weak intersubunit interaction resulting from the loss of the salt bridges. A comparative study with WT reveals that the biphasic transition from I2 to I3, which involves allosteric structural R–T transition in WT, is accelerated in K30D. The reason for the acceleration of the process was scrutinized by subsequent structural analysis. Structural analysis using the SADSs of the three dimer intermediates shows that the tertiary and quaternary structural changes are suppressed in the IK30D2-to-IK30D3 transition compared to WT, in which the I2WT-to-IWT3 transition involves R–T structural transition with dramatic tertiary and quaternary structural changes. The structural similarity between R-like IK30D2 and IK30D3 can account for the acceleration of the biphasic IK30D2-to-IK30D3 transition. The observations from the kinetic and structural analyses indicate that the loss of salt bridges not only leads to the weakening of intersubunit interaction but also interrupts the R–T allosteric structural transition of HbI. Furthermore, the correlation between the bimolecular CO recombination rates and quaternary structures of I3 was inspected for K30D and WT, as well as other mutants studied by TRXSS such as F97Y and T72V. Thereby, it turned out that the bimolecular CO recombination of I3 is abnormally decelerated in K30D, even though I3 of K30D has a structure more similar to the ground state than I3 of WT, F97Y, and T72V. Considering that the bimolecular CO recombination process requires both the structural recovery to the ground state and the binding of CO ligands, the distinct correlation observed in K30D infers that the loss of the salt bridges degrades the ligand-binding affinity of K30D. Therefore, it can be considered that the salt bridges in HbI may play a functional role in ligand binding as well as the structural role contributing to the progression of R–T allosteric structural transition. These comparisons of the structural dynamics and kinetics of K30D and WT offer insights into the role of salt bridges. If interfacial salt bridges serve only as structural constraints to increase the rigidity between two subunits, one may expect that K30D, where the salt bridges are abolished, would have a more flexible intersubunit motion than WT. The data show the opposite; K30D has a suppressed intersubunit motion, suggesting a more sophisticated role of interfacial salt bridges beyond a simple structural glue between subunits. It should be noted that the structural changes of HbI are closely related to allosteric regulation. Once a ligand is attached to the heme of one subunit, this may trigger the change of the ligand-binding affinity of the other subunit via allosteric regulation. This consideration shows that the interfacial salt bridges not only assist the physical connection of two subunits but also play a critical role in the global structural signal transduction of one subunit to the other subunit via a series of well-organized structural transitions.In this study, we applied TRXSS to the K30D mutant of HbI to investigate the structural and functional roles of the salt bridges located at the subunit interface in the allosteric structural transition of HbI. The kinetic analysis of the TRXSS data reveals that K30D has a kinetic framework with three structurally distinct intermediates of the dimer (IH. I. directed the research. H. I. designed research. S. M. performed sample preparation. T. W. K., S. L., and S. K. performed the TRXSS experiments. M. C., J. G. K., H. K., and Y. L. analyzed the data. M. C., J. G. K., and H. I. wrote the paper, and all authors discussed the experimental results.The authors declare no competing financial interests.SC-012-D1SC01207J-s001


Due to the fast global spreading of the Severe Acute Respiratory Syndrome Coronavirus – 2 (SARS-CoV-2), prevention and treatment options are direly needed in order to control infection-related morbidity, mortality, and economic losses. Although drug and inactivated and attenuated virus vaccine development can require significant amounts of time and resources, DNA and RNA vaccines offer a quick, simple, and cheap treatment alternative, even when produced on a large scale. The spike protein, which has been shown as the most antigenic SARS-CoV-2 protein, has been widely selected as the target of choice for DNA/RNA vaccines. Vaccination campaigns have reported high vaccination rates and protection, but numerous unintended effects, ranging from muscle pain to death, have led to concerns about the safety of RNA/DNA vaccines. In parallel to these studies, several open reading frames (ORFs) have been found to be overlapping SARS-CoV-2 accessory genes, two of which, ORF2b and ORF-Sh, overlap the spike protein sequence. Thus, the presence of these, and potentially other ORFs on SARS-CoV-2 DNA/RNA vaccines, could lead to the translation of undesired proteins during vaccination. Herein, we discuss the translation of overlapping genes in connection with DNA/RNA vaccines. Two mRNA vaccine spike protein sequences, which have been made publicly-available, were compared to the wild-type sequence in order to uncover possible differences in putative overlapping ORFs. Notably, the Moderna mRNA-1273 vaccine sequence is predicted to contain no frameshifted ORFs on the positive sense strand, which highlights the utility of codon optimization in DNA/RNA vaccine design to remove undesired overlapping ORFs. Since little information is available on ORF2b or ORF-Sh, we use structural bioinformatics techniques to investigate the structure-function relationship of these proteins. The presence of putative ORFs on DNA/RNA vaccine candidates implies that overlapping genes may contribute to the translation of smaller peptides, potentially leading to unintended clinical outcomes, and that the protein-coding potential of DNA/RNA vaccines should be rigorously examined prior to administration. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a positive-sense single-stranded RNA virus that was first described in late 2019 . The heaThe SARS-CoV-2 genome codes for at least 30 proteins, three of which are exposed on the virion surface and can be recognized by the immune cell system –10. The Numerous companies and academic institutions across the globe have developed or are currently developing DNA/RNA vaccines for the SARS-CoV-2 spike protein . GeneralAlthough SARS-CoV-2 DNA/RNA vaccines have been subjected to health and safety testing prior to bulk dissemination, a diverse assortment of both systemic and local (near injection site) side effects, ranging from mild to severe, following vaccination have been described , 27. SymThe understanding that one mRNA codes for one gene in eukaryotes, not considering alternative splicing, has been challenged after several studies have revealed the presence and translation of multiple open reading frames (ORFs) within one expressed mRNA , 37. AddSeveral ORFs have been found to overlap previously annotated genes in the SARS-CoV-2 proteome . For insSince little has been reported on the functionality of ORF2b or ORF-Sh in infected cells  and no homologous domains were found using sequence analysis tools, we used structural bioinformatics methodologies, as previously performed to elucidate the three-dimensional features of the SARS-CoV-2 proteome, to model the structural characteristics both ORFs –61. As shttps://github.com/NAalytics; https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/). The posting of these data allows direct comparative analyses between the vaccine-formulated and wild-type spike protein sequences.Although the presence of overlapping genes on the wild-type nucleotide sequence of the spike protein challenges the effectiveness of DNA/RNA vaccines, precautionary steps can be taken to prevent the translation of these smaller, internal ORFs. For example, vaccine nucleotide sequences can be selectively codon optimized, as is normally performed to enhance translation efficiency in host tissues, to remove alternative start codons and internal ribosome entry sites, thus preventing non-specific recognition by ribosomal complexes . Codon oComparing the nucleotide sequences of the wild-type and vaccine mRNA spike proteins may reveal the extent to which the sequences have been changed during codon optimization, thus potentially altering translation efficiency of the spike protein and overlapping ORFs. Of note, prior to codon optimization, both companies have reported including proline mutations to stabilize and preserve the spike protein structure, thus implying small changes in spike amino acid content as well. Using the EMBOSS Needle pairwise sequence alignment tool, the wild-type spike sequence (NCBI accession: NC_045512) is found to be 68.7% and 45.3% identical to the mRNA-1273 and BNT162b2 vaccine spike sequences (as opposed to the entirety of the mRNA sequence), respectively, and the mRNA-1273 and BNT162b2 spike sequences are 48.6% identical to one another , which has been noted to be an accurate reflector of gene translation, was calculated for all three spike sequences using the COUSIN and CAIcal web servers –71. As ahttps://www.ncbi.nlm.nih.gov/orffinder/). Although ORF identification using this tool does not imply translation, an overview of the available reading frames may provide insights into coding potential differences between the wild-type and vaccine candidate sequences. Minimum ORF length was set to the default 75 nucleotides, no alternative initiation codons were allowed, and only “ATG” start sites were considered.Considering the extensive codon optimization performed on the vaccine spike sequences, the comparison of putative ORFs in the wild-type and selected vaccine mRNA sequences may shed light on the protein-coding potential of DNA/RNA sequences used in SARS-CoV-2 vaccines. Thus, in order to examine the differences between the available ORFs on the wild-type, Moderna mRNA-1273, and Pfizer BNT162b2 sequences, putative ORFs of all three nucleotide sequences were detected using the NCBI ORFfinder web server  of the protein sequence that code for the most antigenic regions, as exemplified by the Pfizer BNT162b1 mRNA vaccine that codes for a trimeric construct of the receptor-binding domain of the SARS-CoV-2 spike protein . A shortMultimeric vaccine DNA/RNA sequences that include antigenic regions of different viral proteins could also be used to increase immunogenicity while shortening the length of the construct and, thus, controlling for the presence of overlapping ORFs –77. For The use of experimental techniques, such as ribosomal profiling or mass spectrometry, on vaccinated patient or laboratory animal samples or pseudovirus-infected tissue cultures may help determine whether the overlapping ORFs are translated and to what degree they are translated compared to the intended protein. Thus, several potential checkpoints can be utilized to control for the translation of small ORFs within DNA/RNA vaccine candidate sequences. Otherwise, unintended proteins could be translated by the host cell, which may lead to side effects resembling that of viral infection symptoms.DNA/RNA vaccines have proven to be an effective way to develop vaccines quickly for emerging pathogens. However, with a new set of solutions, comes a new set of problems . AlthougThe original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.CB, MB, and AV contributed to conception and design of the study. CB, MB, and AV contributed to sequence and structural analyses. All authors contributed to manuscript writing and revision. All authors contributed to the article and approved the submitted version.TB thanks the Wellcome Trust for support through an Investigator Award . CB was supported by Antibiotic Research UK (PHZJ/687).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. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We found that increasing lying area increased the horses’ use of the lying hall and their total lying time, and that the lying time of a horse housed in a single box was equivalent to the lying time of a horse in group housing with access to a lying area of 18 m2/horse. Hence, to ensure access to sufficient resting space for all horses in group housing, we recommend that the minimum requirement should be reassessed and increased.A safe and comfortable resting area to lie down and sleep in is an important factor in ensuring horse welfare. The lying times of stalled horses depend on factors such as bedding, housing, and lying area, while the sleeping behavior of group-housed horses may be influenced by such factors as social relations and competition for space. This study aimed to analyze time spent in, as well as activity taking place in, lying halls of various sizes. We compared single boxes and open barns with available lying areas of 8, 15, or 18 m2/horse available in the lying halls. The horses’ behavior was video recorded and logged using scan sampling and interval observations. Individual boxes were used as a control. The horses were found to spend longer time in sternal and lateral recumbency in the hall with a lying area of 18 m2/horse than the hall with a lying area of 8 m2/horse. Increasing the area of the lying hall also increased overall time spent there. Consequently, the hypothesis that increasing lying area will increase the horses’ use of the lying hall, as well as their total lying time, was accepted.Sleep is crucial to horses’ wellbeing, and their lying time can vary according to such factors as climate, exercise, bedding, and housing. This study aimed to analyze behavior and time spent in lying halls of various sizes. We examined the influence of housing systems on total lying time and behavior, and how changes to available lying area can affect lying time. Two open barns were used in this study, with lying areas of 8, 15, and 18 m Ensuring horse welfare is vital for riders and owners alike, both for ethical reasons and to ensure their health and happiness. From an ethical perspective, it may be argued that because we use horses for sport and leisure, we have a responsibility to protect their wellbeing, not only in preventing suffering, but also in promoting positive emotional states, as Mellor & Burns  concludeAn important factor in the welfare of horses is the opportunity to get enough sleep. Horses move through four stages of vigilance: wakefulness, drowsiness, slow wave sleep, and paradoxical sleep ,5,6. EveTotal lying time can vary according to several factors. It tends to be shorter, for example, in hot climates , and lon2/horse, 59 ± 48 min) spent significantly less time lying down (p < 0.01) than horses with access to a larger lying area . In two of the open barns, the lying area was divided between two lying halls, and the lying time for those herds did not differ significantly from the other herds. Moreover, Raabymagle & Ladewig [2 m2) compared with larger boxes ((2.5 times withers’ height of the horse)2 m2). In an open barn housed by broodmares, the proportion of the herd using the lying hall increased when the number of horses was reduced, which basically increased the available lying area from 7 to 17 m2 [2/horse for large horses (determined by height of the withers over 1.71 m) when they are being fed outside the lying hall, and 10 m2/horse when they are being fed inside [Fader & Sambraus  found th Ladewig  found thto 17 m2 . The samd inside . Besidesd inside . Lying td inside . Using rd inside . Werhahnd inside  found thFurthermore, horses’ use of lying halls seems to be determined by the weather, and wet and windy conditions have been shown to increase the use of lying halls ,29,30. FOn this basis, we believe there is demand for research into the “best practice” for planning and managing animal welfare in open barns, which should not be dictated solely by minimum requirements. Sleep is an important factor in the welfare of horses and lying time for stalled horses seems to vary according to such factors as climate, exercise, bedding, housing, and lying area. To ensure the wellbeing of the horses in this type of housing system, it is critical that lying areas provide the opportunity for adequate rest and sleep. The aim of this study was to analyze time spent in, as well as activity taking place in, lying halls of varying sizes. The study had three research questions: How does the use of lying halls depend on available lying area? In what way does the lying time differ in individual boxes compared to shared lying area in an open barn? How do variations in total available lying area affect lying time? The hypothesis was that increasing available lying area would increase total lying time. ® . Within this system, haylage and concentrate were delivered by automatic computer-controlled feeding stations, as described by Kjellberg & Morgan [To answer the questions, we designed two different studies performed at two of the Swedish National Equestrian Centers: Flyinge (Study 1) and Strömsholm (Study 2). In both stables, the horses were housed in an open barn system for 24 horses and fed using the feeding technique from HIT Active Stable& Morgan . The feeAll 18 geldings, aged 6–21 years, including five focal horses, aged 10–16 years, were school horses at the equine center, used for riding or driving at the university equine bachelor program or the upper secondary school. They were all Swedish Warmblood, except for one horse which was a North Swedish draft horse. The horses were trained 5 days/week at an intensity dictated by their education, with 1 day/week spent hacking. All horses were well accustomed to each housing system and had spent at least two months in the actual open barn before the study.2 and one lying hall with a lying area of 280 m2  and the number of visits to the lying hall (Method 1 and 2). The time budget was calculated for the five focal horses (Method 1). The proportion of observations of the horses’ locations  was calculated both for the entire day and for day or nighttime. The difference between day and night was analyzed using a Student’s t-test. The significance level was set to All 11 geldings, aged 3–17 years, were Swedish Warmblood and were used as riding horses at the university equine bachelor program. The horses were trained 3–4 days/week at an intensity dictated by their education, with 2 days/week spent hacking, except for the 3-year-old. The horses were 1.62–1.74 m over the withers in height. All horses were well accustomed to each housing system and had spent at least two months in the actual open barn before the study.2 (3 × 3.5 m) was used as a control. This stable was situated immediately next to the active open barn. When the horses were housed in the single boxes, they were fed forage individually four times a day, and concentrate twice. They spent 4–6 h daily in a paddock. Each single box had an automatic watering ball and was permanently bedded with wood shavings. The horses could socialize with their neighboring horses through nose contact via grids.A stable with 25 single boxes, each with an area of 10.5 m2 and had a total indoor lying area of 460 m2 divided into four lying halls, with the possibility of closing individual lying halls  was registered, before moving back to the open barn with its different available lying areas in the lying halls during periods 2 and 3. In period 2, the horses only had access to Lying hall 1 with an available lying area of 8 m2/horse. In period 3, they had access to two lying halls , with available lying areas of 18 m2 per horse. In periods 2 and 3, there were always ten horses housed in the open barn. In total, eleven horses were recorded using video observation across all three periods, but only eight horses participated in all three periods. Consequently, these eight horses were included in the statistics. The video recording was continuous, but all observations were logged with scan sampling, in 5 min blocks based on the ethogram . No video recordings were conducted in the paddock when the horses were housed in single boxes in period 1 or the lying halls in period 2 or 3. Four cameras were used during period 1a and 1b, with one camera filming each single box. Two cameras were used during period 2 for video recording Lying hall 1, and four cameras during period 3 for video recording Lying halls 1 and 2, two in each lying hall.The results of Study 2 are based on mean values, from observations which took place during 72 h of video recording in period 1 and 2. There were only 48 h of video recording in period 3, as the horses tore down the barriers to a closed lying hall on the last night. Statistical analyses were conducted using RStudio . The data were processed using a Poisson regression with horse as the variable factor and treatment as the fixed factor, using model: glmer1<-glmer (LieS~beh + (1|Namn), data = hast, family = “poisson”). To ensure that the variance was not the same as the mean and the analysis, a negative-binomial distribution was made according to the model: glmer.nb1<-glmer.nb (LieS~beh + (1|Namn), data = hast).p = 0.004). The horses visited the lying hall an average of 5.7 (ranging from 3.6 to 7.0) times every 24 h. The five focal horses used in method 1 spent an average of 22% (ranging from 19.5 to 24.5%) of each 24 h period in the lying hall (p < 0.001). The lying hall was never empty , the average number of horses in the lying hall was 4.1 ± 1.6 . The lyiThe five focal horses exhibited the following behaviors: sternal recumbency (18%), lateral recumbency (8%), standing rest (45%), standing attentive (25%), and active (4%) during the time spent in the lying hall. 2/horse, compared to 33% of observations when the available lying area was 18 m2/horse (p < 0.0001)  in the larger lying hall (100 m2). In Study 2, in 14% of observations the horses were found in the lying hall when the available lying area was 8 m2/horse) was least frequented at 8 AM, 13–14 PM and 17–18 PM, and most frequented in the early morning, at 3 AM  and the single boxes  (2 and 18 m2 (p ≤ 0.0001). There was no difference in total resting time  between the lying halls with different lying areas per horse (8 m2 and 18 m2). Moreover, none of the horses were seen lying down in the paddock during the period in which they were housed in single boxes.The horses’ lying times for both sternal and lateral recumbency were significantly lower in the smaller lying hall with an available lying area of 8 mctively) . Standin2/horse than when they had access to an area of 8 m2/horse (p < 0.0001)  . There w2/horse in Study 2 was chosen based on the minimum requirement for a horse of a particular size stipulated by Swedish legislation [Housing horses in an open barn system may substantially improve their welfare, as Yngvesson et al.  found, pislation . The minThe highest number of horses was found during the night (1–4 AM) in all lying halls, indicating that horses will seek shelter during nighttime whatever the available lying area, as seen in another study . Since oThere was a wide range of time spent in the lying hall among the focal horses in Study 1, ranging from 6.6% to 35.2% of their time, during separate 24 h periods. This variation in use of lying halls is consistent with other studies and can vary due to weather or feed availability . The lyi2) to the smaller one (80 m2). This indicated that even a small increase in the lying area had an impact on the time spent in the lying hall. Although most of the horses spent all or nearly all of their time in the larger lying hall, some individuals preferred the smaller hall. In opting for the smaller hall, these horses had access to a greater lying area for themselves, instead of being squeezed in with the other horses in the lying hall next door. There could be several reasons for this choice. Some studies have found that a larger lying area leads to more frequent lying down [2). Consequently, we can speculate that this horse might have found it too crowded in the lying hall and required more individual space even though neither individual spacing nor rank was measured in this study.In Study 2, the horses’ use of the lying hall increased from 14% to 33% when the available area increased. In comparing time spent in the two lying halls during period 3, we find that the horses preferred the larger lying hall  and in the lying halls with a larger available lying area (18 m2/horse), than they did in the lying halls with a smaller available lying area (8 m2/horse). Still, the estimated average lying time in the single boxes was slightly lower (2 h and 40 min) than the length of 3 to 4 h reported in other studies [2/horse, due to competition for space. In addition, they would have had a better environmental overview outside the lying hall. Another factor that could have increased the lying time in the single boxes was that the bedding in the single boxes was shavings, which may have led to increased lying time as the horses were not inclined to forage from their straw bedding as they were in the lying halls.In Study 2 we found that the horses demonstrated longer estimated sternal and lateral recumbency in the single boxes  and daily work levels, using another five horses may have affected the result due to individual differences. When using scan sampling, there is a possibility of missing some details, such as the duration between the logging of each observation and behaviors exhibited in those intervals. It is hoped, however, that these disadvantages are compensated for by the ability to analyze more animals in less time. In Study 2 there is missing data for one 24 h period in which the horses had access to a lying area of 18 mThe active barn system is suitable for urban horse-keeping and for riding schools with limited access to land for paddocks and grazing and meets all the horse’s basic needs. Overall, however, our observations suggest that the current minimum lying area requirements (at least those stipulated by Swedish legislation) might be too small for the domesticated horse, and that the minimum requirement could be increased in order to better safeguard their welfare. Our recommendation, in order to improve the welfare of horses kept in open barns, is to increase the minimum requirement of the lying area by a factor of 20–100%. This recommendation is based on the finding that to offer a lying area extended by only 20%  encouraged the horses to prefer that specific lying hall. Furthermore, increasing the available lying area increased both the use of the lying area and the lying time. Since it is crucial to the welfare of horses that they have the opportunity to get enough sleep, the choice of bedding is an important factor to consider. However, further studies are needed to ascertain the optimal lying area per horse and bedding material in a lying hall, in order to maximize the sleeping comfort of each individual.In this study conducted in Sweden in two active open barns, we found that the horses’ lying time increased when they were given access to an individual space in a single box or a larger common space. Increasing the area in the lying hall also increased the use of the lying hall. The hypothesis that increasing the lying area will increase the horses’ total lying time was accepted.


The 5′-UTR of HIV-1 genomic RNA is known to form specific structures and has important functions. There are three 5′-terminal sequences, G1, G2 and G3, with different localizations in the cell and virion particles as well as different efficiencies in translation and reverse transcription reactions. In the present study, the structural characteristics of the joint region between the TAR and PolyA stems was analysed, and it was found that small differences in the 5′-terminus affect the conformational characteristics of the stem-loop structures. In the G1 form, the two stems form a coaxial stem, whereas in the G2 and G3 forms, the two stems are structurally independent of each other. In the case of the G1 form, the 3′-flanking nucleotides of the PolyA stem are included in the stable coaxial stem structure, which may affect the rest of the 5′-UTR structure. This result demonstrates that the local conformation of this functionally key region has an important role in the function of the 5′-UTR. The RNA transcribed from each transcription initiation site is called G1 form, G2 form, or G3 form1. The G1-form is dominant in virus particles, and the G3 form is dominant in the cytosol of infected cells1. It has also been shown that the G2 and G3 forms are enriched in polysomes2. Furthermore, under physiological-like ionic conditions, the 5′-leader RNA in G1 form adopts a dimeric structure, whereas the 5′-leader RNA in the G2 or G3 form exists predominantly as a monomer2. Our previous work also showed that the number of G residues in the 5′-terminus affects the reverse transcription reaction1. These facts indicate that the function of HIV-1 RNA is controlled by a small difference in the 5′-terminal sequence2.In the promoter of HIV-1 provirus DNA, each of three continuous G residues works as the transcription initiation site4. For the G1 form transcript, the 5′-Cap structure was hidden to prevent interaction with eIF-4E4, which may be the reason that the G1-form transcript was not concentrated in the ribosome fraction. In contrast, the G2 and G3-form transcripts were suggested to adopt a more flexible conformation in the PolyA stem, which allows the formation of extended central stems, including U5 and DIS, preventing dimerization4. Although the global structures of the 5′-terminus of the transcripts were determined, it is important to analyse the effect of the number of G residues on the local structure of the 5′-terminus of the HIV-1 transcripts at the nucleotide level using suitable model RNAs.Recently, Brown et al. analysed the structures of the 5′-region of RNA transcripts by NMR spectroscopyIn the present study, we designed model RNAs corresponding to the junction region Fig. A and ana7GpppGmUm) and GGU were not different from each other  and TP-G3G-38  were similar to each other, one was higher than that of TP-G1G-36 (66.6 °C), and the other was lower. The UV melting curves for the sub-fragments were also measured  and PolyA-17 (62.4 °C). The higher Tm value of TP-G2G-37 was similar to that of TAR-G2G-22 (73.9 °C) rather than TAR-G2G-21 (69.6 °C). Thus, it was suggested that TP-G2G-37 consists of two independent structural units, TAR-G2G-22 and PolyA-15. This is also true for TP-G3G-38 and its sub-fragments, TAR-G3G-23 and PolyA-15, indicating that A60 stacks to the terminal GC base pair of the TAR stem for TP-G2G-37 and TP-G3G-38. By comparing the Tm values of the three fragments, it was found that, in the case of TP-G1G-36, the two stems were equally stable, whereas, in the case of TP-G1G-36 and TP-G2G-37, the TAR stems were more stable, and the PolyA stems were less stable.Then, the thermal melting profiles of the three RNAs were examined. In the first derivatives of the melting curves Fig. A, one peed Table . The Tm 15N–1H correlation signal for G3 could be observed for G3 labelled TP-G1G-36 but not for TP-G2G-37 and TP-G3G-38 in the 15N–1H SQC spectra (data not shown). Thus, it is possible that the two stem loops interacted with each other for TP-G1G-36 but not for TP-G2G-37 and TP-G3G-38. It was also indicated with A60-labelled TP-G2G-37 and TP-G3G-38 that the 13C–1H correlation signals of A60 in these two RNAs were not observed at 283 K, probably due to exchange broadening (data not shown). Then, the solution structure of TP-G1G-36 was determined as described below. For TP-G3G-38, signals due to the four G residues at the 5′ terminus were not clearly observed, and two signals were observed for each of G52-54, suggesting conformational polymorphism in the terminal region , which is first synthesized in the reverse transcription reaction, were abundantly generated from G3-form RNA, but dramatically reduced from G1-form RNA1. Furthermore, 5′-Cap significantly increased the strand-transfer efficiency of cDNA generated from G1-form RNA5. A previous study by Chen and Menees demonstrated that yeast Ty1 retroelement RNA formed a lariat structure through a 2′–5′ bond between the 5′- and 3′-ends of RNA and might play roles in Ty1 reverse transcription at the first strand-transfer step6. Thus, it must be elucidated how the structure and/or stability of the TAR-PolyA region affect the reverse transcription reaction as well as packaging. Our model RNA system may work in such analyses as shown in the present work.Our previous work showed that the G1 form is dominant in virus particles and that the G3 form is dominant in the cytosol of infected cells7 and the predicted secondary structures of the three RNAs in the G1, G2 and G3 forms by vsfold58 and centroid fold9, an RNA fragment, TP-G1G-36, was designed  was assumed to form a base pair with C59. In this case, the sub-fragment for TAR starts with A60. Therefore, TP-G2G-37 was divided at C59 and A60 to form TAR-G2G-21 and PolyA-16. In addition, in consideration of the possibility that A60 stacked to the stem on the TAR side, TP-G2G-37 was also divided at A60 and C61 to form TAR-G2G-22 and PolyA-15. In the case of TP-G3G-38, the G residue at the 5′ end (G0), although it is not complementary, was assumed to form a GA base pair with A60 and stacked with the G1-C59 base pair. Therefore, TAR-G3G-38 was divided at A60 and C61 to form TAR-G3G-23 and PolyA-15.Fragments corresponding to the TAR and PolyA sides of TP-G1G-36, TP-G2G-37 and TP-G3G-38 were also designed  or 60 (A60) for TP-G1G-36, TP-G2G-37 and TP-G3G-38. The 13C/15N-labelled phosphoramidite units were purchased from Taiyo Nippon Sanso Co., Ltd., and oligonucleotides were synthesized by Hokkaido System Science Co., Ltd. The labelled RNA fragments were used only for NMR measurements.For all RNA fragments, chemically synthesized oligonucleotides were purchased from Hokkaido System Science Co., Ltd. It is noted that the fully The purchased fragments were dissolved in water. For NMR measurement, the counterions were exchanged by the ultrafiltration method: the synthetic RNAs were concentrated by Vivaspin with a molecular weight cut-off value of 3000  and then exchanged in buffer solution.The purity and conformation of each RNA were checked by native and denatured PAGE, respectively. Gels were stained with SYBR Gold (Thermo Fisher Scientific). Prestain Marker for Small RNA Plus (BioDynamics Laboratory Inc.) was used as the size maker for the denatured PAGE. Only bands for 20 and 30 nt were stained by SYBR Gold.Tm) were obtained. The buffer solution condition of each RNA sample was 20 mM sodium phosphate buffer (pH 6.5) containing 50 mM NaCl. Each sample was prepared as 115 μL with an absorbance at 260 nm of 0.2–0.3 at 25 °C. Melting curves at 260 nm were measured with a V-730BIO UV–Vis spectrophotometer (JASCO corporation) from 25 to 95 °C at 1 degree/min. The measurements were repeated three times for each RNA fragment. The Tm values were calculated using the program Spectra Manager (JASCO corporation). The Tm values were confirmed by curve fitting with the second-order function for each peak in the first derivatives of UV melting curves by a homemade program.UV melting curves were measured for the RNA fragments and the melting temperatures .RNA samples were dissolved in 20 mM sodium phosphate buffer (pH 6.5) containing 50 mM NaCl with 5% D10.One-dimensional and 2D spectra, including HOHAHA and NOESY spectra were measured at 283 K. For TP-G1G-36, TP-G2G-37 and TP-G3G-38, imino proton spectra were measured at 283 K, 298 K and 310 K. NMR spectra were measured using an AVANCE-600 spectrometer and analysed with the programs Topspin 3.5 (Bruker BioSpin) and SPARKY11. Distance constraints were derived from the NOE volumes with the mean of the volumes of pyrimidine H5–H6 signals as the measure. For the base pairs for which imino proton signals were observed, plane-related restraints and the hydrogen bonding distance constraints of the base pair were prepared. In addition, for the stem region confirmed by the imino proton signals, dihedral angle restraints for the main chain, sugar packer and rotation around the glycosidic bond were prepared to form the RNA-A conformation. Furthermore, restraints for the C2′-endo form based on the intense HOHAHA signal between H1′ and H2′ and for the syn form based on the intensities of NOE between H8/H6 and H1′ were generated. The structure calculation was performed by the program CNS 12 with the protocol described previously13. Tertiary structure calculations were performed for TP-G1G-36, TAR-G1G-19, TAR-G2G-22, PolyA-17 and PolyA-15.The tertiary structure calculation was performed with the conventional method14. The minimized averaged structure obtained by CNS_SOLVE was used as the Initial model. The charge for the RNA molecule was neutralized by adding sodium ions and the RNA was surrounded by TIP3 water molecules in a box with a buffer distance between the wall of the box and the closest atom in the solute of 9.0 Å. The numbers of sodium ions and water molecules were 35 and 7080, respectively. The equilibration calculation was performed as described previously13. The productive simulation in constant volume without positional restraints was performed for 10 ns . The trajectory of the productive simulation was processed by the program ptraj in the AMBER suite14 and visualized by the program Chimera15.The molecular dynamics (MD) simulation was performed with the program AMBER127GpppGmUm by UCSF Chimera. An MD simulation with 10 ns was also performed for TP-G1Cap-36. The numbers of sodium ions and water molecules are 36 and 7075, respectively.An RNA with the Cap structure, TP-G1Cap-36, was modelled by replacing the 5′-GGU sequence of TP-G1G-36 with mSupplementary Information.


Existing small noncoding RNA analysis tools are optimized for processing short sequencing reads (17–35 nucleotides) to monitor microRNA expression. However, these strategies under-represent many biologically relevant classes of small noncoding RNAs in the 36–200 nucleotides length range . To address this, we developed DANSR, a tool for the detection of annotated and novel small RNAs using sequencing reads with variable lengths (ranging from 17–200 nt). While DANSR is broadly applicable to any small RNA dataset, we applied it to a cohort of matched normal, primary, and distant metastatic colorectal cancer specimens to demonstrate its ability to quantify annotated small RNAs, discover novel genes, and calculate differential expression. DANSR is available as an open source tool. A diverse range of small noncoding RNA  species have been shown to contribute to human development and diseases . HoweverThe ability to sequence variable read lengths can provide important insights concerning a class of small noncoding RNAs, as exemplified by tRNA fragments of varying lengths . HoweverTo accurately discover and quantify small RNA 17–200 nt) expression, the DANSR tool executes six steps A: (1) Cu–200 nt ehttps://github.com/ChrisMaherLab/DANSR, accessed on 25 October 2021). The input of DANSR includes sequencing reads from a sample in FASTQ/FASTA format and annotated small noncoding RNAs in GTF format  and 10 distant liver metastases with matched adjacent normal liver tissue  using a We also used DANSR to discover >2900 previously unannotated small RNAs . To exemAltogether, of the more than 7700 small RNAs (both the above annotated and unannotated RNAs as well as >1000 in close proximity to known annotations) detected by DANSR within the CRC cohort, 60.9% were intronic, 29.8% were intergenic, and 9.3% overlapped known exons of protein coding genes or long noncoding RNAs (lncRNA). Examples of DANSR’s successful detection of annotated small RNAs overlapping protein coding exons are illustrated in We also took advantage of the distinctive, hairpin-loop structure exhibited by miRNAs to sift through the set of unannotated small ncRNA gene candidates to identify novel miRNAs. RNAfold 2.4.14 was used to predict the secondary structure of the candidate genes from their corresponding DNA sequences . These sUsing all previously annotated and novel small noncoding RNAs, we discovered differentially expressed (DE) RNAs in colorectal cancer as shown in To filter false positive clusters caused by multi-mapped reads, DANSR uses its network and decision tree models to identify the original loci of small ncRNA reads that map to large numbers of homologous regions across the genome. To evaluate our claim that the approach used by DANSR to analyze multi-mapped reads allows for more sensitive and accurate results, we compared DANSR with a naïve approach that simply removes all multi-mapped reads shared between nodes of the network graph from consideration, assuming they cannot contribute meaningfully to the discovery of small RNA clusters. In order to assess DANSR’s performance, we sought to benchmark against a wide array of existing small RNA discovery tools. The majority of such tools proved to be unusable for our current application due to input file size, read length, or small RNA type restrictions  and the matching ncRNA annotation file (ncRNA_hg38.gtf). The tags of the GTF file were appended to include the transcript_id field expected by DANSR. To facilitate a fair comparison, lncRNA and protein coding genes, which are automatically filtered from DANSR’s results, were removed from the set of input reads, and clique graphs from DANSR’s network model, where a single group of reads is identified as aligning to a group of repetitive regions, were designated as a true positive (TP) if the group contained the origin of the simulated reads. As in the Manatee paper, a false negative (FN) was defined as a case in which a small RNA with simulated transcript count >5 was estimated as 0 by a tool; a false positive (FP) was a case in which a tool estimated transcript counts >5 for a small RNA whose true value was 0. To accurately build the network to handle multi-mapped reads and apply the decision tree model to classify RNA species, it is critical to accurately estimate the boundaries and lengths of the small RNA candidates. This issue was less critical when the focus was on the length range of 17–35 nt, where the longest small RNA is only about twice the size of the shortest RNA. However, for the full length range of 17–200 nt, the longest RNA can be ~12 times longer than the shortest RNA. The advantage of DANSR’s boundary optimization algorithm can be seen by comparing the length distribution of expressed annotated small RNAs identified by DANSR in the primary colon tumors to those identified by ShortStack  ManateeA. In conTo assess the reliability of de novo detections of small RNAs by DANSR in our deep-sequenced CRC cohort, we used the miRNA data (enriched for 17–35 nt) from TCGA colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) cohorts as a validation cohort . Figure The TCGA miRNA enriched cohorts also provide an opportunity to independently benchmark DANSR’s performance in the discovery of annotated miRNAs. DANSR successfully detects 99% (466 out of 473) of miRNAs that TCGA reports as being expressed within either TCGA-COAD or TCGA-READ (defined as at least one sample with >5 RPM by TCGA), as shown in Our study shows DANSR is a novel tool that represents a valuable addition to existing methods in the area of small ncRNA detection, quantification, and discovery. DANSR implements the algorithms that have been empirically established through our prior ncRNA discovery studies, making these powerful methods easily available to users . FurtherWe have demonstrated the effectiveness of the DANSR model by applying it to a deep-sequenced CRC cohort consisting of colon tumor, matched normal, liver metastasis, and liver normal samples whose library preparation was designed specifically to facilitate the discovery of both short (17–35 nt) and mid-length (36–200 nt) ncRNAs . DANSR wWe examined the results of a comparison between DANSR and two alternative tools, Manatee and ShortStack. In addition to DANSR’s more complete output, with full annotation and cluster range information, we saw that both tools returned many thousands of results that DANSR’s algorithm rejected as low-quality A. ManualFinally, we used data from the TCGA-COAD and TCGA-READ studies as a positive control to benchmark DANSR’s ability to detect known and novel small ncRNAs. We ran DANSR directly on these data and detected, as expected, over 99% of the known miRNAs identified by TCGA C. This fThe DANSR tool is implemented in the Python and C++ programming languages and employs six main steps A: (1) adIn the optional first step, DANSR trims user-supplied adapter sequences from the submitted reads using Cutadapt , retainiThe next step aligns the reads to the human reference genome using BWA ver. 0.7.17 . The aliIn the third step, clusters are identified based on the overlap between read alignments, with several tunable quality filters applied to the reads before clustering. First, reads are filtered on their CIGAR string such that only insertions or deletions of lengths no more than 5 are allowed, and all indels must be flanked by alignments of lengths of at least 5 . Second, aligned reads are grouped into uniquely mapped reads, multi-mapped reads, and repeat reads. Unique and multi-mapped reads are retained, while repeat reads are excluded from further analysis; the number of alignments above which a read is designated as repeat and discarded is controlled by the number-hits parameter (default 5). To form clusters of reads, the retained reads are first converted into BED format and overlapped alignments are merged into read clusters using BEDTools (2.27.0 or later) . Clusterith base in a read of length n, the read weight can be represented by the following equation:cov = total number of reads covering the base.In the fourth step, a heuristic algorithm is employed to optimize the boundaries of each read cluster identified in the third step to more accurately define the range of the small RNA cluster and split it into sub-clusters if it represents more than one feature. . This alOnce all read weights are computed, reads within the cluster are ordered by weight, and a number of the lowest-weight reads equal to a proportion of the total number of reads  in the original cluster, designated In the fifth step, a network model is built based on uniquely aligned and multi-mapped reads to identify single-node read clusters and multi-node read clusters  C, which Each node of the network is a cluster of read alignments, and two nodes are connected by a link if >50% of the alignments from at least one node have a hit in the other node. Fully connected sub-networks come in two shapes: star-like and clique. A star-like sub-network represents the case in which a central small RNA node generates false positive read clusters at multiple locations, which are difficult to filter in the absence of network analysis. A clique shape represents a small non-coding RNA that has multiple copies on the human genome. The network analysis ensures that, on the one hand, no copy of the same small non-coding RNA is missed and, on the other hand, that the expression level of the small non-coding RNA is calculated accurately by including reads from repetitive small RNAs only once.At the same time as each cluster is processed by the network model, it is annotated using a human reference annotation and one or more small RNA annotation sets . DANSR provides a GRCh38/hg38 small RNA library that includes annotations for >187 k features and is sourced from RNAcentral . A curatTo evaluate the quality of clusters and reduce the false positive rate, a decision tree model has been constructed to classify clusters (graph nodes) into three main categories: (1) annotated, (2) unannotated, and (3) low-quality clusters D. The deOnce all clusters are classified, low-quality clusters are written to the output file of rejected candidates and the remaining read clusters are further refined. Among annotated clusters (including single-node clusters that did not pass through the decision tree), if the highest Jaccard score among its provisional annotations is greater than a cutoff value , that annotation is applied to the cluster and it is written to the annotated output file. Clusters with scores below this value that fall within the length range 17–200 nt and that do not overlap with protein coding exons are designated as falling in close proximity to a known annotation and written to the close proximity output file; otherwise, the clusters are written to the rejected file. Clusters identified as unannotated are filtered for length and written to the unannotated output file.In this paper’s application, we have improved the protocol  using BlThe isolated RNA was purified using the RNA Clean and Concentrator kit  per the manufacturer’s instructions, including the in-column DNase I treatment. The concentration of the RNA was determined using the Qubit RNA BR Assay kit . Yields ranged from 500 ng–2 μg. The quality of the RNA was determined using the RNA 6000 Nano kit . The RNA samples had an RNA Integrity Number (RIN) of 4 or higher.® Multiplex Small RNA Library Prep Set for Illumina  following the manufacturer’s protocol. During PCR amplification, a unique index was added to each sample. Each amplified sample was purified using a QIAQuick PCR Purification column . The purified DNA was eluted from the column in 32 μL of Nuclease-Free Water. The concentration of the purified DNA was measured using the Qubit dsDNA HS assay kit . The size distribution was determined by running an aliquot of each library on the Agilent Bioanalyzer High Sensitivity DNA Analysis assay . The remaining library sample was then size-selected on a Blue Pippin 3% Agarose Gel Cassette, dye-free, with internal standards  to enrich for library fragments 17–200 nt in length. The concentration of the enriched fragments was determined using the Qubit dsDNA HS assay kit . The fragment size distribution was verified by running an aliquot of the enriched fragments library on the Agilent Bioanalyzer High Sensitivity DNA Analysis assay .Small RNA libraries were generated from 1 μg inputs of purified RNA using the NEBNextIn this article, we reported the design and implementation of a novel analysis tool, DANSR, that is able to analyze reads of various lengths to monitor diverse classes of small noncoding RNAs (17–200 nt). As proof of concept, we demonstrated how DANSR can reveal the landscape of known and novel small noncoding RNAs relevant throughout metastatic colorectal cancer progression. However, our optimized tool for small noncoding RNA can be applied broadly to any small RNA sequence data in human development or disease.


Currently, RPS contains 21,613 LLPS-related RNAs with three different evidence types, including ‘Reviewed’, ‘High-throughput’ and ‘Predicted’. RPS provides extensive annotations of LLPS-associated RNA properties, including sequence features, RNA structures, RNA–protein/RNA–RNA interactions, and RNA modifications. Moreover, RPS also provides comprehensive disease annotations to help users to explore the relationship between LLPS and disease. The user-friendly web interface of RPS allows users to access the data efficiently. In summary, we believe that RPS will serve as a valuable platform to study the role of RNA in LLPS and further improve our understanding of the biological functions of LLPS.Liquid–liquid phase separation (LLPS) is critical for assembling membraneless organelles (MLOs) such as nucleoli, P-bodies, and stress granules, which are involved in various physiological processes and pathological conditions. While the critical role of RNA in the formation and the maintenance of MLOs is increasingly appreciated, there is still a lack of specific resources for LLPS-related RNAs. Here, we presented RPS ( Liquid-liquid phase separation (LLPS) is a reversible process driving the formation of membraneless organelles (MLOs) such as nucleoli, P-bodies and stress granules (SGs). During the LLPS process, many biomolecules including protein and RNA aggregate together into biomolecular condensates, which play critical roles in the regulation of many biological processes such as cellular stress responses , homeostDeciphering the components of LLPS is vital to understand the roles of LLPS in physiological and pathological processes. Previous studies primarily focused on the protein components of LLPS. It has been demonstrated that G3BP protein was required for SG formation in mammalian cells during oxidative stress , and theet al. (6A), as the most prevalent mRNA modification, was considered as a multivalent scaffold for binding YTHDF proteins in mammalian cells , a comprehensive database of LLPS-related RNAs in 20 distinct biomolecular condensates across eukaryotes and viruses. RPS contains 21 613 LLPS-related RNAs derived from literature mining, high-throughput analysis and predictions based on interaction network  Reviewed: The literature mining was performed by searching NCBI PubMed using keywords combinations ‘((phase separation[Title/Abstract]) OR (phase transition[Title/Abstract]) OR (biomolecular condensates[Title/Abstract]) OR (MLOs[Title/Abstract]) OR (membraneless organelle[Title/Abstract])) AND (RNA)’. We retrieved a total of 1507 papers published up to 30 June 2021, then screened out 131 publications that describe biomolecular condensates and related RNAs by rechecking abstracts manually. Both natural and synthetic RNAs with this evidence type were curated. (ii) High-throughput: RNA composition of MLOs under different conditions can be identified by RNA sequencing methods. By retrieving related keywords (same with ‘Reviewed’ type) from the GEO , EMBL-EBhttps://github.com/FelixKrueger/TrimGalore) to remove low-quality bases (Phred score < 25) and adapters contamination. The trimmed reads were aligned to the reference genome  using STAR (v.2.7.6) with default parameters , electron microscopy (EM), etc., experiment conditions such as RNA/salt concentrations, salt, pH, pressure, and temperature, protein components. It should be noted that ‘Predicted’ LLPS-related RNAs are not assigned to a particular LLPS process, therefore having no corresponding LLPS information.RPS provides various basic information of LLPS-related RNAs, such as gene symbol, gene ID, transcript ID, sequence, RNA type, functional description. These data were preferentially extracted from the original publications, otherwise, they were acquired from Ensembl  or UCSC 6A sites prediction, DeepOME (5C) sites prediction and ISGm1A for N1-methyladenosine (m1A) sites prediction.To improve our understanding of how RNA contributes to phase separation, RPS integrated multiple resources and tools to annotate distinct LLPS-associated properties of RNAs. Sequence-specific features, including tandem repeats and RNA sequence families, were detected separately using tandem repeat finder  and Rfam DeepOME  for 2′-O DeepOME  and PPUS DeepOME  for pseu DeepOME  for 5-meTo discover potential relations between LLPS-related RNAs and diseases, we collected RNA-disease associations with experimental evidence from OMIM , DisGeNETo better curate the deposited data, we categorized all biomolecular condensates into three classes: (i) nucleus, including paraspeckle, Cajal body, DNA damage foci, histone locus body, nuclear body, nuclear speckle, nucleolus, PML nuclear body, PcG body; (ii) cytoplasm: centrosome, P-body, cytoplasmic granule, G body, neuronal granule, stress granule and TIS granule; (iii) others, including germ cell condensates  and RNP granules in unknown locations, as well as coacervate droplets. These phase-separated condensates were further annotated with Gene Ontology (GO) cellular component terms.All data in RPS were stored and managed by MySQL tables. The server-backend development was based on java and the web-frontend interfaces were implemented in Hyper Text Markup Language (HTML), Cascading Style Sheets (CSS) and JavaScript (JS). In order to present data more efficiently and intuitively, multiple statistical diagrams were embedded in the website. The interactive heat maps showing the expression abundance and differential expression werehttp://genome.ucsc.edu/)  .Reviewed’: 523 entries for 337 RNAs were validated to participate in LLPS through in vitro or in vivo experiments. Among them, 328 entries are from 214 natural RNAs. The other 123 synthetic nucleotides with 195 entries were designed for condensate reconstruction experiments, such as homotypic RNA polymers. (ii) ‘High-throughput’: 20 153 RNAs involved in 18 different LLPS processes were identified by high-throughput analyses. These RNAs were considered as RNA components of MLOs such as SGs, paraspeckles, P-body, G-body and other RNA granules (Table S1). (iii) ‘Predicted’: 1358 RNAs were predicted as potential LLPS-related RNAs based on RNA-associated interactiozns, where the transcripts of NEAT1, BRCA1, NORAD, MALAT1 and ACTB were supported by three types of evidence. Besides, total 199 ‘Predicted’ LLPS-related RNAs were validated either by ‘Reviewed’ or ‘High-throughput’ evidence  ‘e Figure , demonstP-value < 0.01, Wilcoxon signed-rank test), indicating the promoting effect of RNA length on LLPS. In addition, comparisons of multivalent interaction sites (RNA–RBP and RNA–RNA binding sites) showed that LLPS-related RNAs of all three evidence types have significantly more interactions sites than those in non-LLPS-related RNAs . This suggests that increased multivalent interactions are likely to promote LLPS, which is in line with previous findings , indicating that RNA-mediated phase separation is closely related to human diseases.To characterize the LLPS-related RNAs, we systematically compared several RNA properties of LLPS-related and non-LLPS-related RNAs in humans. The non-LLPS-related RNAs were defined as human canonical transcripts that were not deposited in RPS. Consistent with previous studies ,63, the findings . Moreoves Figure , which ian cells . In termRPS provides a user-friendly web interface, allowing users to explore LLPS-related RNAs or processes of interest easily and interactively Figure .Browse, users can browse the entries by condensates. For each condensate, the browse page presents a statistical chart, as well as diagrams for the LLPS-related RNAs distribution in distinct organisms and RNA types. The query results can be filtered by extra options, including Organism, Evidence type, RNA type and other RNA features (Figure s Figure .Search, RPS provides two ways to search the database (Figure e Figure . First, Detail, the details of each entry are displayed on the ‘Detail’ page by clicking any RPS ID or LLPS ID (Figure D Figure . The detAll data deposited in RPS are available on the ‘Download’ page. Detailed guidance on the usage of RPS can be found on the ‘Help’ page.6A modifications, as well as multivalent interactions were likely to promote LLPS (Figure Phase separation has expanded our understanding of biochemical reactions and biological processes in MLOs. With the advancement of phase separation research technology, more and more RNAs have been found to participate in LLPS process and regulate the assembly of MLOs. Emerging evidence has shown that RNA is crucial in sensing stress stimulations, signal transduction, and maintenance of phase separation. Despite that, existed LLPS-associated databases primarily focus on proteins , while aS Figure –D. TheseS Figure ,23,63,64S Figure , suggestTaken together, we anticipate that RPS will support investigations into the potential of RNA in condensate biology and develop better therapeutic treatments for phase-separation-related diseases. With the growing interest in the role of RNA in LLPS, there will be a rapidly increasing number of relevant studies and more LLPS-related RNAs will be discovered. Therefore, we are dedicated to ensuring the long-term maintenance and reliability of the RPS database by continuous updates and careful validation.http://rps.renlab.org.RPS is a comprehensive online database available at gkab986_Supplemental_FileClick here for additional data file.