The mechanism that leads to liquid-liquid phase separation (LLPS) of the tau protein, whose pathological aggregation is implicated in neurodegenerative disorders, is not well understood. Establishing a phase diagram that delineates the boundaries of phase co-existence is key to understanding whether LLPS is an equilibrium or intermediate state. We demonstrate that tau and RNA reversibly form complex coacervates. While the equilibrium phase diagram can be fit to an analytical theory, a more advanced model is investigated through field theoretic simulations (FTS) that provided direct insight into the thermodynamic driving forces of tau LLPS. Together, experiment and simulation reveal that tau-RNA LLPS is stable within a narrow equilibrium window near physiological conditions over experimentally tunable parameters including temperature, salt and tau concentrations, and is entropy-driven. Guided by our phase diagram, we show that tau can be driven toward LLPS under live cell coculturing conditions with rationally chosen experimental parameters. Proteins make up much of the machinery of cells and perform many roles that are essential for life. Some important proteins – known as intrinsically disordered proteins – lack any stable three-dimensional structure. One such protein, called tau, is best known for its ability to form tangles in the brain, and a buildup of these tangles is a hallmark of Alzheimer’s disease and many other dementias.Tau is also one of a number of proteins that can undergo a process called liquid-liquid phase separation: essentially, a solution of tau separates into a very dilute solution interspersed with droplets of a concentrated tau solution, similar to an oil-water mixture separating into a very watery solution with drops of oil. Understanding the conditions that lead to spontaneous liquid-liquid phase separation might give insight into how the tau tangles form. However, it was not known whether it is possible in principle for liquid-liquid phase separation of tau to occur in a living brain.Lin, McCarty et al. have now used an advanced computer simulation method together with experiments to map the conditions under which a solution containing tau undergoes liquid-liquid phase separation. Temperature as well as the concentrations of salt and the tau protein all influenced how easily tau droplets formed or dissolved, and the narrow range of conditions that encouraged droplet formation fell within the normal conditions found in the body, also known as “physiological conditions”. This suggested that tau droplets might form and dissolve easily in living systems, and possibly in the brain, depending on the precise physiological conditions. To explore this possibility further, tau protein was added to a dish containing living cells. As the map suggested, slightly adjusting temperature or protein concentrations caused tau droplets to form and dissolve, all while the cells remained alive.The map provided by this study may offer guides to researchers looking for liquid-liquid phase separation in the brain. If liquid-liquid phase separation of tau occurs in living brains, it may be important for determining whether and when damaging tau tangles emerge. For example, the high concentration of tau in droplets might speed up tangle formation. Ultimately, a better understanding of the conditions and mechanism for liquid-liquid phase separation of tau can help researchers understand the role of protein droplet formation in living systems. This may be a process that promotes, or possibly a regulatory mechanism that prevents, the formation of tau tangles associated with dementia. Protein liquid-liquid phase separation (LLPS) is a process in which proteins assemble and partition into a protein-dense phase and a protein-dilute phase. The proteins in the dense phase form droplets, and retain liquid-like mobility, as shown by NMR measurements . The proIn a healthy neuron, tau is bound to microtubules. When tau falls off the microtubule under adverse conditions to the cell, tau is solubilized in the intracellular space as an IDP. Under certain conditions, tau forms intracellular fibrillary tangles, a process linked to neurodegenerative tauopathies that include Alzheimer’s disease. In recent work, we showed that tau in neurons strongly (nanomolar dissociation constant) and selectively associates with smaller RNA species, most notably tRNA . We alsoIn this work, we characterize the phase diagram of tau-RNA LLPS using a combination of experiment and simulation, and thereby specify the conditions that drive the system toward a homogeneous phase or an LLPS state. We study a N-terminus truncated version of the longest isoform of human 4R tau in vitro, and first demonstrate that tau-RNA complexation is reversible, and that tau remains dynamic and without a persistent structure within the dense phase. The phase coexistence curve separating a supernatant phase from a condensate phase is determined by the system's free energy, which in turn is state dependent, that is dependent on concentration, temperature, salt, and the nature of the interaction strength between the various solution constituents, including the solvent. We construct the phase diagram from cloud-point measurements of the onset of complex coacervation under varying conditions of temperature, salt, and polymer concentrations. These experiments establish the features and phase coexistence boundaries of the phase diagram, which we then model using theory and simulation to rationalize and understand the physical mechanisms that drive and stabilize LLPS.A number of theoretical models can be used to model LLPS, each with their own advantages and disadvantages. Ideally, one would turn to simulations at atomic resolution in explicit solvent; however, such models are computationally prohibitive given the multiple orders of magnitude in time and length scales involved in LLPS. Turning to the polymer physics literature, theoretical treatments of simplified coarse-grained models are much more computationally tractable, and offer useful insight. Although approximate, analytical theories can be formulated, providing an extremely efficient platform for describing the thermodynamics of polyelectrolyte mixtures . These iOf all the models described above, fitting experimental data with the FH or FH-VO theory is currently the preferred methodology in the LLPS community to describe and analyze phase diagrams. We demonstrate that this model can be fit to describe our experimental data, but the learning outcome from this modeling is limited. Thus, we take a different approach by computing the exact phase diagram of an off-lattice coarse-grained polyelectrolyte model using field theoretic simulations (FTS). FTS is a numerical approach that allows one to fully account for fluctuations, and thus to compute equilibrium properties from a suitably chosen coarse-grained representation of the true system without the need for analytical approximation. The ability to perform field theoretic simulations enables us to include the important physics of polymer sequence-specificity that cannot be captured by FH-VO, including charge distribution and chain connectivity. Results from FTS are compared to those obtained from the FH-VO model.The model substantiates the experimental phase diagram that the equilibrium window for the complex coacervation of tau and RNA under cellular conditions is narrow. Guided by the phase diagram, empirically obtained from in vitro experiments and validated by simulation, we finally show that LLPS of tau-RNA can be established and rationalized under cellular co-culturing conditions in the presence of live cells.Truncated versions of the longest isoform of human 4R tau, residues 255–441  and resivice versa   upwards and downwards, while the absorbance at λ = 500 nm was monitored, referred to as turbidity hereafter. Ramping rates of 0.5°C/min and 1°C/min were tested, but the results shown to be indistinguishable. Microscopy images were concurrently acquired at low and high turbidity, confirming the appearance and abundance of CC droplets correlating with turbidity increase, and ce versa . The turr (LCST)  (Siow etr (LCST) . By cyclr (LCST) . The trar (LCST) , even thr (LCST)  by verifIt is understood that upon gradual heating of the solution phase, the mechanism of LLPS proceeds via a nucleation process , and henTo understand the principles and governing interactions driving tau-RNA CC formation, we constructed a phase diagram for tau187-RNA CC by measuring the transition temperature – to be described in greater detail below – as a function of protein concentration and salt concentration. We first recorded tau187-RNA turbidity at various [tau], [RNA] and [NaCl] values, ranging from 2 to 240 μM, 6–720 μg/mL and 30–120 mM, respectively. Titrating RNA to tau187, the turbidity was found to be peaked when [RNA]:[tau] reached charge matching condition at which the charge ratio between net positive and negative charges was 1:1 (which for tau187 and RNA used in this study corresponded to [tau187]:[RNA]=1 μM: 3 μg/mL), validating once more that LLPS is driven by complex coacervation (CC) . Hencefocp, could be extracted as shown in cp was determined from heating curves out of practical utility; Tcp from cooling curves is possibly closer to thermodynamic transitions). The experimental cloud-point temperature Tcp for CC formation as a function of [tau] and [NaCl] are shown (as points) in cp, favoring CC formation, while increasing [NaCl] raises Tcp, disfavoring CC formation. Such trends were observed at two [NaCl] and two [tau] values, respectively  for tau187, and 28 μM, 150 mM and 20°C for tau114. These two experimental conditions were used in the next section for comparing the two constructs of tau.The features of the Tau-RNA CC phase diagram were also investigated by comparing tau187 and tau114. Tau187-RNA CC and tau114-RNA CC were prepared with 20 μM tau187 and 28 μM tau114, so that the total concentration of polymer, that is tau and RNA, reaches 0.5 mg/mL. Turbidity was recorded at varying [NaCl]. Similar to the observation with tau187-RNA CC, tau114-RNA CC showed decreasing turbidity at increasing [NaCl] . The   and Tcp s [NaCl] . For thecp than tau187-RNA CC, suggesting it is more favorable to phase separation. This qualitatively agrees with experimental observations. Notice that the shorter tau114 has a slightly higher propensity to form CC as compared to the longer tau187 fragment, an observation that is opposite of what one would expect from purely entropic considerations based on the mixing of homopolymers or simple coacervation. One possible explanation could be the increased charge density of tau114 with respect to tau187, indicating the importance of both charge sequence and charge density for the phase diagram. Additional short-ranged sequence-specific interactions between tau114 and RNA that are not present in tau187 is another possibility that is not considered in the present model.Binodal curves for tau114-RNA CC were also computed and are compared with tau187-RNA CC, along with experimental data . Comparib in a coarse-grained bead-spring polymer model. The charge of each segment is unambiguously assigned from the particular amino acid charge at pH 7.0. In addition to harmonic bonds between nearest neighbors, which enforces chain connectivity, all segment pairs interact via two types of non-bonded potentials: a short-ranged excluded volume repulsion and a long-range electrostatic interaction between charged monomers  on a physically motivated polyelectrolyte model , in whicmers see . We takemers see . Converse is the unit of electronic charge,  where Bl. FTS is performed in implicit solvent with a uniform dielectric background. We assume that the polymer chains are in a fully dissociated state, and we do not explicitly represent counter ions. The effect of excess salt is included in our model by introducing point charges explicitly, which engage in Coulomb interactions with all other charged species and repel other ions and polymer segments at short distances by the same Gaussian excluded volume repulsion. By introducing explicit small ions in this manner, we are neglecting strong correlations such as counter ion condensation; however, we are allowing for weak correlations of the Debye-Hückel type. The explicit addition of salt will serve to screen the electrostatic interactions and inhibit the driving force for CC, in agreement with the experiments.The main features of the model used for FTS here are the inclusion of chain connectivity, charge sequence-dependence for the electrostatic interactions based on the primary amino acid sequence of tau, solvation effects which are parameterized by the single excluded volume parameter, Details of the FTS protocol are described in the Materials and methods. By performing FTS at various state points and computing equilibrium properties, we first set out to fully explore the parameter space relevant for LLPS in this model. This involves running simulations at different conditions analogous to experiments. For each simulation, the thermodynamic state of the system is determined by specifying a particular value for the dimensionless excluded volume parameter v and the Bjerrum length Bl. A direct comparison between FTS and the experimental phase diagram will be deferred until the following section. The phase coexistence points  for a given value of the excluded volume v and Bjerrum length Bl can be obtained by running many simulations over a range of concentrations, and finding the concentration values at which the chemical potential and the osmotic pressure are equal in both phases  can readily and reversibly induce complex coacervation in vivo under physiological conditions.Despite the simplicity of the coarse-grained description, the model predicts that these two competing parameters, excluded volume vs. electrostatic interactions, are nearly balanced around physiological salt concentration, temperature, and protein concentration. Assuming that the relative dielectric constant for water is shown in , at the v and Bjerrum length b to be approximately the distance between the bBl can be estimated at the experimental cloud point temperature directly from v.In the preceding section, we presented the phase diagram from FTS explicitly in terms of the model parameters of the excluded volume v can be related to the residue-residue non-Coulombic interaction potential asThe excluded volume parameter v and Bl to the experimental temperature, we can compare directly the FTS with the experimental results . Simulations performed in this manner with explicit salt are shown as open green squares in To demonstrate that FTS of tau-RNA LLPS can be applied to consider the effect of post translational charge modification of tau, we tested FTS of phosphorylation as an example. We first phosphorylated tau187 in vitro using mouse brain extract, and confirmed the occurrence of phosphorylation using SDS-PAGE and western blot analysis  it is principally feasible for cells to tune the formation of tau-RNA CCs. This has important implications for studying the physiological roles of tau-RNA CCs, and thus we asked if tau-RNA CCs could indeed exist in a biologically relevant media in the presence of living cells. Both the FH-VO theory and FTS predict that the conditions of high-protein concentration, low ionic strength, high temperature and high crowding reagents  would iThe ability of tau to undergo LLPS via a mechanism of complex coacervation has been recognized in a number of recent publications . HoweverAlthough the FH-VO model cannot model spatially varying charges along the peptide backbone, we were able to fit the experimental data by treating the Flory-Huggins χ parameter as an empirical, temperature-dependent, adjustable parameter. This result highlights the fact that the FH-VO model is adaptable to experimental data. Still, the FH-VO model has limited predictability and should be seen as a qualitative descriptor of phase separation. In contrast, FTS is an approximation-free analysis that can provide physical insight and predictive information for biopolymers, such as scaling relationships and polymer or protein sequence effects. As shown above, the tau-RNA phase diagram was successfully reproduced by FTS using model parameters that are reasonable estimates of the experimental physical conditions. With reasonable estimates for the parameters in our polymer model  . Substitex is small and positive. We hypothesize that the positive, that is nonionic, enthalpy value for forming a coacervate phase is due to the requirement of breaking favorable interactions between hydrophilic residues and water that stabilizes the solution phase of tau (ΔHex = -ΔHtau/water). For comparison, the enthalpy of forming a hydrogen bond ΔHHB at room temperature is ~ −8 kJ • mol−1 , there has to be a source of penalty in the form of a positive ΔHex value; the unfavorable ΔHex associated with tau-RNA CC might come from the loss of hydrogen bonds in the hydration shell from overlapping and sharing of the tau hydration shells in the dense CC phase.Notably, ΔH • mol−1  while th• mol−1; . Given tnoncomb value is also small, positive and of comparable magnitude as ΔHex, making temperature increase a facile modulator favoring tau-RNA CC. Given the positive value of ΔHex for tau-RNA CC, the entropy gain upon phase separation is contributing to the driving force of tau-RNA CC formation (besides the electrostatic correlation energy between the polycationic and polyanionic polymer segments that is the major driving force). Looking to potential origins for positive TΔSnoncomb, we consider the entropy gain of breaking a hydrogen bond of TΔSHB ~ 6 kJ • mol−1  physiological conditions. Our in vitro experiments found the tau-RNA CC phase diagram boundary to lie near physiological conditions. This suggests that tau-RNA CC can occur in vivo upon modulation of parameters, such as the local temperature, electrostatic balance, including local pH, and osmotic pressure. We demonstrate that indeed tau-RNA CC can be achieved in co-culture with living cells. While the coexistence of tau and RNA at low (10 μM) polymer concentrations is not sufficient to drive CC in cellular media, the addition of a molecular crowding reagent is, under physiological conditions . While i00 mg/mL ) could blive cells. Interestingly, we find that without any adjustable parameters our simulations predict that tau-RNA is positioned near the binodal phase boundary around physiological conditions. This suggests that small and subtle changes within the cellular environment may be sufficient to induce LLPS in otherwise healthy neurons. Even if the conditions that induce LLPS in the cell is transient, the LLPS state can facilitate irreversible protein aggregation if aggregation-promoting factors are already available, giving credence to the idea that LLPS may play a role in neurodegenerative diseases. However, we speculate that LLPS is reversible in the majority of biological events that drive LLPS, making it hard to observe this state within the cellular context.We report here the first detailed picture of the thermodynamics of tau-RNA complex coacervation. The observation of an LCST phase diagram implies that although electrostatic interactions are key to CC formation, factors that contribute to solvation entropy gain are key to driving liquid-liquid phase separation. We have computed the first approximation-free theoretical phase diagram for tau-RNA complex coacervation from FTS, where we introduced a temperature-dependent excluded volume term. Simulations show a competition between electrostatic strength  and excluded volume . This knowledge can be used to design experiments that perturb this parameter space in vivo, as well as predict or understand biological mechanisms that may be favorable towards liquid-liquid phase separation. As a proof of this concept we have shown that by deliberately changing salt concentration, temperature, and solvent quality (by the addition of PEG), we can make tau-RNA LLPS appear or disappear in cellular medium with Unless stated, a 20 mM ammonium acetate buffer at pH 7.0 was used and referred to here as final buffer. Tau, RNA, NaCl, PEG and other stocks were prepared using final buffer. Measurements were taken in final buffer at room temperature unless stated.E. coli BL21 (DE3) cells previously transfected were cultured from frozen glycerol stock overnight in 10 mL luria broth (LB) which was used to inoculate 1 L of fresh LB. Culturing and inoculation were performed at 37°C with shaking of 200 rpm. At OD600 of 0.6–0.8, tau187 variant expression was induced by incubation with 1 mM isopropylß-D-thiogalactoside (Sigma Aldrich) for 2–3 hr. Cells were harvested by centrifugation for 30 min at 5000 × g , and the pellets were stored at −20°C until further use.N-terminal truncated, microtubule binding domain containing tau187 (residues 255–441 with a His-tag at the N-terminus) were used for in vitro studies. The cloning, expression, and purification have been previously described . The sin2 (10 mM) and incubated for 30 min on ice. Lysate was then heated to 65°C for 13 min, cooled on ice for 20 min and then centrifuged to remove the precipitant. The supernatant was loaded onto a Ni-NTA agarose column pre-equilibrated with wash buffer A . The column was then washed with 20 ml of buffer A, 15 ml buffer B . Purified tau187 was eluted with buffer C  supplemented with varying amounts of imidazole increasing from 100 mM to 300 mM. The protein was then concentrated via centrifugal filters  and the buffer was exchanged into final buffer by PD-10 desalting column . The final protein concentration was determined by UV-Vis absorption at 274 nm using an extinction coefficient of 2.8 cm−1mM−1, calculated from absorption of Tyrosine [3].Cell pellets were resuspended in lysis buffer  with 1 Pierce protease inhibitor tablet (Thermo Fisher). Lysis was initiated by the addition of lysozyme (2 mg/ml), DNase (20 µg/ml), and MgClFreshly eluted tau187C291S (with one cysteine at site 322) was replaced in final buffer using a PD-10 desalting column . Protein after PD-10 was labeled overnight at 4°C by immediately mixing with a 10-fold molar excess of the spin label  methanethiosulfonate , resulting in spin labeled tau (tau187C291S-SL). Excess label was removed using PD-10. The protein was concentrated using centrifugal filter  and the final protein concentration was determined by UV-Vis absorption at 274 nm as mentioned above. Non-labeled tau187C291S was used in order to achieve spin dilution.Cw EPR measurements were carried out using a X-band spectrometer operating at 9.8 GHz  and a dielectric cavity . 100 μM tau187C291S-SL was mixed with 400 μM tau187C291S to reach 20% spin labeling. Samples under droplet forming condition were prepared by adding 1.5 mg/mL RNA, and tau samples under aggregation-inducing conditions prepared by adding 125 μM heparin . A sample of 4.0 μL volume was loaded into a quartz capillary  and sealed at both ends with critoseal, and then placed in the dielectric cavity for measurements. Cw EPR spectra were acquired by using 6 mW of microwave power, 0.5 gauss modulation amplitude, 100 gauss sweep width, and 8–64 scans for signal averaging.xx = 6.2 G, Ayy = 5.9 G, Azz = 37.0 G, and gxx = 2.0078, gyy = 2.0058, and gzz = 2.0022.The recorded cw EPR spectra were subjected to single- or double-component simulation. EPR simulation and fitting were performed using MultiComponent, a program developed by Christian Altenbach . For all spectra fitting, the magnetic tensors A and g were fixed and used as constraints as previously reported . These vR was calculated using τR = 1/(6R). For tau-heparin aggregates, the cw EPR were subjected to double-component simulation, where the parameters of the fitted single-component were used as a mobile-component. The immobile-component were set to be identical to the mobile-component, except the diffusion tensor tilt angle βD = 36° and the order parameter S. The fitting parameters were limited at a minimum, which includes the population, p, rotational diffusion constants of mobile- and immobile-component, R1 and R2, and the order paramter, S of the immobile-component. The fitted immobile-component were used to represent the rotational correlation time for tau-heparin fibrils. For tau-RNA CC. the cw EPR spectra were subjected to both single- and double-component fitting. Comparing the two fitting schemes showed that singl-component fitting has almost overlapped the cw EPR spectra, while double-component fitting results in a immobile-component population of ~10% (data not shown). This showed that tau-RNA CC cw EPR spectra can be sufficiently fit with single-component. The fitted rotational correlation time was calculated and plotted against tau-heparin samples.For soluble tau, the cw EPR spectra were best fitted with a single-component simulation and the rotational diffusion constant (R) can be extracted. The rotation correlation time τ500), using a Shimadzu UV-1601 spectrophotometer (Shimadzu Inc). The amount of coacervates in a sample were approximated to be propotional to its OD500.Turbidity of samples at room temperature were represented by optical density at a 500 nm wavelength (OD500 measured using Jasco J-1500 CD Spectrometer (JASCO Inc) equipped with temperature controller and spectrophotometer. 120 μL of 20 μM tau187C291S, 60 μg/mL polyU RNA and 30 mM NaCl in working buffer were prepared in a 100 μL cuvette (Starna Scientific Ltd) and kept at 4°C for 5 min before cycling. Heating and cooling temperatures were ramped at 1°C/min while OD500 was monitored.Tubidity of samples at ramping temperatures were represented by ODBright field images were examed to confirm the presence of tau-RNA CC. 100 μM tau187C291S and 300 μg/mL polyU RNA was mixed in presence of 20 mM ammonium acetate and 30 mM NaCl. 10 μL of the mixture was pipetted onto a microscope slide with a cover slide gapped by two layers of double-sided sticky tape. Temperatures were controlled using an incubator. Bright field images were acquired using a spectral confocal microscope .It was shown by fluorescence microscopy in protein-RNA LLPS that protein is concentrated inside the droplet . For repDue to the difficulty of preparing large volume of pure dense phase, we can only underestimate the tau and polyU concentration in dense phase. Since tau and RNA have different UV absorbance spectra, fitting spectra of the tau-RNA mixed sample with those of pure tau and polyU generated the concentration of both. Fitting results showed that over 99% of the tau and over 99.9% of polyU were condensed inside the dense phase. This partitioning guaranteed that the property of tau in the droplet suspension represents those in the droplets.max × MW of protein / [protein]×εdye).Protein (tau187 or K18) was labeled with Alexa Fluor 488 or 555 5-SDP ester (Life Technologies) according to the suppliers instructions. After labeling, 100 mM glycine was added to quench the reaction and the proteins were subjected to Zeba desalting columns (Thermo Scientific) to remove any unreacted label. Average label incorporation was between 1 and 1.5 moles/mole of protein, as determined by measuring fluorescence and protein concentration (A2 at 37°C. Tau protein (1:20 labeled K18:unlabeled tau114), RNA, PEG, and media  were mixed at the indicated concentrations and added to cells at varying temperatures. Images were obtained on a Leica SP8 Resonant Scanning Confocal.H4 neuroglioma cells (ATCC HTB-148) were cultured in DMEM supplemented with 10% FBS, 100 μg/mL penicillin/streptomycin. Cultures were maintained in a humidified atmosphere of 5% CO2, 5 mM EGTA, 2 mM DTT, 2 mM ATP, 1 μM okadaic acid, protease inhibitors). After incubation, samples were centrifuged and the supernatant was buffer exchanged using zeba desalting columns (Thermo Fisher) into buffer . Concentration was determined by BCA assay. Phosphorylation was confirmed using a western blot assay to look at phospho-epitopes 396/404 using PHF-1 Antibody (Peter Davies).Phosphorylation of tau was performed as previously described . In briei refers to one of the five species. iN is the degree of polymerization for species i. For tau187 and tau114, Ni equals to the length of the polypeptides  treatment, where the polymer system is mapped onto a lattice. Voorn and Overbeek extended the FH formalism to polyelectrolytes by including long-ranged electrostatic interactions with a Debye–Hückel term. The resulting expression for the free energy of mixing (ΔGmix) per lattice site ispeptides ; while fi is the average charge per monomer, which is determined by (net charge)/Ni. The net charges of tau at experimental pH conditions (pH = 7) were estimated based on primary sequences in i for other species were listed in i is fixed. We also consider a modified version, a FH-VO-CR model, where σi of RNA is set to a function of temperature as discussed further below.σϕi is the volume fraction of species . ϕi was computed by i is the molar concentration and cw the molar concentration of pure water computed from water volume molarity: cw = 55.56 mol/L. In experiments, ctau and cRNA were designed to reach a 1:1 charge ratio, therefore, we have NRNA ×cRNA = 11 × ctau = 11×[tau]. In addition to NaCl, there is 20 mM ammonium acetate in the buffer. The total monovalent salt concentration is i were calculated from experimental [tau] and [NaCl] as,In w, B the Boltzmann constant and T the absolute temperature.α is the strength of the electrostatic interactions defined asij is the Flory-Huggins interaction parameter between species i and j, which will be defined and discussed below.The three terms on the right-hand side of cp, was assigned to the cloud point of the sample. Tcp was determined by fitting normalized turbidity-temperature curves to a sigmoid function as followsA phase separation temperature, Tmix depends on four variables: total polymer volume fraction dentical , leavingA binodal composition curve  was computed by finding the bi-tangent points of nleqslv using Newton-Ralphson algorithm at given initial guess. Finally, the A bi-tangent pair quations ,{∂∂ϕ|Φ=Φn total polymers made up of b. The solvent is treated implicitly with a uniform dielectric background j on chain v is the excluded volume parameter  and The advantage of the coarse-grained polyelectrolyte model employed in this work is that it can be exactly converted to a statistical field theory by utilizing a Hubbard-Stratonovich transformation as described in ectively . In the We stress that no additional approximations are made in moving from a particle-based model to a statistical field theory. The advantage of such a transformation is that the pairwise interactions between monomers are decoupled in favor of interactions between monomers and a complex-valued field. This transformation is particularly suited to our purposes here as conventional particle simulations can only study the earliest stages of protein aggregation.Field theoretic simulation (FTS) has been widely used to model synthetic polymers , includig=R0N1/2 . Simulatsh of 32  sites. Ash of 32  with Δt=l indexes the chain type (tau or RNA). Additionally, we require the fraction of chains of each type The thermodynamic state of the system is fully determined by specifying a dimensionless excluded volume parameter l is In order to compute the phase coexistence points from FTS, we need to compute the chemical potential As discussed in the main text, the interaction parameter is decomposed into an entropic and enthalpic contribution From the relation Values in the main text are computed using a water volume fraction of  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Naama Barkai as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal his identity: Carlos Castaneda.Thank you for submitting your article "Narrow equilibrium window for complex coacervation of tau and RNA under cellular conditions" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This is an interesting and thorough study that combines experiment, theory, and simulations to understand the physical basis of complex coacervation of tau-protein and RNA. This work established a phase diagram for the tau:RNA polymer system experimentally using cloud point temperatures as a function of polymer (tau:RNA) and salt concentrations and modeled the coacervation of this system to match the experimental data using coarse-grain field theoretic simulations (FTS). This work provides a new framework to understand the thermodynamic underpinnings of complex coacervation for the tau:RNA system.Essential revisions:cp values between 14 and 20 °C? The factors that could give rise to this variability need to be discussed. This is important for these data to be used to test computational models. One concern is the use of ammonium acetate as a buffer at pH 7 for all of their experimental data. The understanding is that ammonium acetate is used to buffer at low pH (acetic acid/acetate pKa 4.7) and high pH (ammonium pKa 9.2), but not at pH 7. How do the authors control for pH effects here? Could the pH effect be a factor that underlies the apparent discrepancy in Figure 2B?1) Important to this study was the collection of "equilibrium" phase transition experiments for cloud point temperature measurements. A more accurate determination of equilibrium perhaps would be to sit at a particular temperature and obtain the turbidity values rather than perform a temperature ramp experiment where temperature was increased 0.5C/min or 1C/min. This is an issue that concerns the experimental reproducibility . For example, in Figure 2B, it appears that there were 5 independent experiments at 30 mM NaCl and 5 μm Tau w/ RNA, that generated T2) Subsection “Tau-RNA complex coacervate phase diagram”, last paragraph: The experimental results indicate that tau114  has a higher propensity to undergo liquid-liquid phase separation (LLPS) with RNA than tau187 (207 residues). Can the authors suggest a plausible physical reason for this behavior? If one considers homopolymer LLPS or simple coacervation, propensity to LLPS tends to decrease with decreasing chain length, which is not being observed here for tau-RNA. Are there differences in sequence-specific features between tau114 and tau187 that may provide a plausible rationalization for the experimentally observed trend?3) In the authors' treatment using Flory-Huggin-Voorn-Overbeek , a temperature-dependent Flory chi parameter  is used to fit the data and to produce the experimental observation that the tau-RNA LLPS tendency increases with increasing temperature . This phenomenon, which is also observed for elastin, is similar to cold denaturation of globular proteins, and its possible physical origin has been discussed recently in the context of biomolecular LLPS by Lin et al., 2018; see Figure 2C of this reference] in terms of the expected temperature-dependence of hydrophobic interactions. The strength of effective attraction between nonpolar groups tends to increase with increasing temperature, as has been demonstrated from model compound transfer experiments, a fact that has long been applied to theory of protein conformational transitions, see, e.g., Dill, Alonso and Hutchinson, 1989. The same trend has also been seen in explicit-water simulations of potentials of mean force of nonpolar solutes in water, see, e.g., Figure 4 in Dias and Chan, 2014. Beyond introducing the temperature-dependent chi parameter as a mere necessity for fitting data, it would be very helpful to the readers to include this perspective (and the above-cited references) in the authors' Discussion to provide a plausible physical rationalization for the observed trend.4) The statement "high crowding reagents (acting to lower the excluded volume)" is problematic. What do the authors mean by the statement? Could the current formulation of this statement be related to the aforementioned somewhat confused concept about excluded volume presented in the manuscript? Would it make more sense to say that crowding agents act to lower the chain configuration entropy of the dilute phase?5) In the field theoretic treatment (subsection “Field theoretic simulations of a coarse-grained model of tau-RNA complex coacervation”), the temperature dependent attraction in the form of (1-theta/T) is presented as a temperature-dependent excluded-volume potential. Although it may be mathematically convenient to introduce temperature dependence as a modified "excluded volume" term, doing so is physically inaccurate and conceptually misleading. Excluded-volume interaction is a hard-core repulsion. Inter-chemical-group attractive interactions such as hydrophobic effect do not abolish the hard cores of the atoms. They just affect the potential energy landscape close to but beyond the hard cores. With this in mind, while the usage of a single Gaussian function for this "excluded volume" energy may be a mathematical simplification that is necessary for field theoretic formulations [the Γ(r) function in the last paragraph of the subsection “Coarse grained polyelectrolyte model used in FTS”], it is important to recognize that it is not entirely physically accurate . The reason is that in the authors' set up the molecular hard cores can be penetrated but in reality they cannot, and this can have consequences on the predictions of volume fractions, etc. Casting the solvent-mediated attraction as a reduction in excluded volume is also conceptually inconsistent with FH-VO because the FH excluded volume is approximately accounted for by the mixing entropy term, and the introduction of the chi parameter does not envision penetrating any excluded volume. Although this point is largely conceptual in the present context and the reviewers do not suggest redoing the field theory simulation using a more realistic excluded volume potential, it is still importance to have conceptual clarity in the authors' Discussion so as to avoid, e.g., oxymoronic statements such as “the excluded volume becomes attractive”. With a recognition of this limitation, the authors may also wish to qualify their statement regarding the "approximation-free" nature of their simulations .6) Discussion, third paragraph, schematic equation: It would be much clearer to view the overall attractive interactions among tau proteins and RNA as an "effective free energy of interaction" that contains both enthalpic and entropic parts (wherein the entropic contribution comes largely from solvent entropy), and this effective interaction free energy  is then balanced by configurational/conformational entropy of the chains . The authors may wish to adopt such a narrative.7) More discussions about controls relevant to their FTS simulations is needed to evaluate the FTS methodology.Bl) parameter obtained from Equation 3 (there is no Equation 3 in the manuscript)? How Equation 2 can be used to obtain Bl if the relative dielectric constant term still needs to be determined? This is a very important section of the paper and a more detailed discussion is needed here.8) When the authors compared simulation to experiment (subsection “Comparison between simulation and experiment”), how was the Bjerrum length (9) In the conclusion, the authors state that the simulation provides "knowledge that can be used to design experiments that perturb this parameter space in vivo". It would be extremely useful if the authors test FTS and their results by considering tau phosphorylation. Even just modeling tau phosphorylation without RNA would be useful. Additional discussion along this line, if attainable in a timely fashion, is preferred, but not required by the reviewers and the editors.Bl is plotted in Figure 6A. Could the authors confirm in the figure legend that this is just a plot of Equation 2 and no temperature dependence of the relative permittivity rε was introduced in Figure 6A?10) In Figure 6C, for the 120mM NaCl data, the simulated transition temperatures (open green squares) have a significantly higher rate of decrease with respect to increasing [tau] than the corresponding experimental data (blue circles). Could the authors provide a plausible reason for this discrepancy? The Bjerrum length  Essential revisions:cp values between 14 and 20 °C? The factors that could give rise to this variability need to be discussed. This is important for these data to be used to test computational models. One concern is the use of ammonium acetate as a buffer at pH 7 for all of their experimental data. The understanding is that ammonium acetate is used to buffer at low pH (acetic acid/acetate pKa 4.7) and high pH (ammonium pKa 9.2), but not at pH 7. How do the authors control for pH effects here? Could the pH effect be a factor that underlies the apparent discrepancy in Figure 2B?1) Important to this study was the collection of "equilibrium" phase transition experiments for cloud point temperature measurements. A more accurate determination of equilibrium perhaps would be to sit at a particular temperature and obtain the turbidity values rather than perform a temperature ramp experiment where temperature was increased 0.5C/min or 1C/min. This is an issue that concerns the experimental reproducibility . For example, in Figure 2B, it appears that there were 5 independent experiments at 30 mM NaCl and 5 μm Tau w/ RNA, that generated TWe recognize the reviewer’s concerns that there is a certain level of variability in our data that constitute the phase diagram. Repetition of experimental data points confirms that some level of variability appears inherent to these measurements, and that we are currently unable to control all sources to reduce this variability. We discuss two potential sources underlying this variability.We observed a steady decrease of turbidity when incubating the tau-RNA mixture under LLPS conditions. We found that the addition of an RNase inhibitor slowed down the decrease in turbidity, while the presence of RNase enzyme accelerated this trend. Based on our control experiments, we conclude that the steady decrease of turbidity results from RNA degradation from RNase contamination. RNase degradation is difficult to control in practice, and can easily contribute to the variability observed, as the reviewer correctly pointed out.We also took up the reviewer’s comment to avoid ammonium acetate to buffer at pH 7. Since we had used ammonium acetate buffer for our measurements, we tested the pH response of ammonium acetate in the presence of tau or RNA alone. We found in the experimental range of tau and RNA concentration  that the pH drifted from 6.72 to 6.62 and 6.68 for tau and RNA respectively. Adding the same concentration of tau or RNA shifts HEPES buffer pH from 7.06 to 7.02 . CompariSince we are currently unable to generate replicate data with smaller variability, we are working with a relatively high number of independent repeats to increase confidence in the tau-RNA phase diagram data. For example, if we take the same data as the reviewer took in Figure 2B, 5 independent measurements span 14 ºC to 20 ºC, with the average value constrained around 17 ºC and a standard deviation of ± 2 ºC. While not perfect, such data range was of sufficient quality to fit the temperature-dependent excluded volume parameter (FTS) or the temperature-dependent Flory-Huggins parameter (Voorn-Overbeek model) to a linear model and determine the location of the experimental data range within the theoretically determined phase diagram model.To extract an experimental phase diagram with higher resolution, pH might have to be exceptionally tightly controlled, temperature tightly controlled and RNA degradation prevented, to name three possible factors that we can think of controlling in future measurements.Besides variability, we also seriously considered the suggestion to determine equilibrium by sitting at a particular temperature and monitoring the turbidity values over time. By doing so, we have observed a steady decrease of turbidity which results from RNA degradation as written above. Even if turbidity is steady, it can result from absorbance of insoluble tau aggregates and thus cannot prove reversibility. Due to the technical challenges of controlling RNA degradation and the ambiguity of turbidity values we used spin labeling and electron paramagnetic resonance to accurately show tau is in a steady state, while using turbidity over temperature ramping along with microscopy to demonstrate tau-RNA CC is reversible and to estimate the phase separation conditions.To address these comments, we added in the main text:cp, which can result from pH fluctuation of ammonium acetate buffer upon tau/RNA addition, as well as RNA degradation as demonstrated in Figure 1—figure supplement 2.”“We point out that there is certain level of variability in the observed T2) Subsection “Tau-RNA complex coacervate phase diagram”, last paragraph: The experimental results indicate that tau114  has a higher propensity to undergo liquid-liquid phase separation (LLPS) with RNA than tau187 (207 residues). Can the authors suggest a plausible physical reason for this behavior? If one considers homopolymer LLPS or simple coacervation, propensity to LLPS tends to decrease with decreasing chain length, which is not being observed here for tau-RNA. Are there differences in sequence-specific features between tau114 and tau187 that may provide a plausible rationalization for the experimentally observed trend?It is indeed true that there is a higher entropic penalty for shorter polymers to phase separate. This usually results in a lower salt concentration being sufficient to screen electrostatic interactions and dissolving the coacervate phase, as the reviewer correctly points out. This trend of decreased LLPS propensity with decreasing chain length has been observed in peptide/RNA experiments (see (1)). Although tau114 is shorter than tau187 , it carries a similar net positive charge at pH ~ 7. If we assume a uniformly smeared out charge distribution, the average charge per amino acid is about twice as that of tau187 . Consequently, under the same tau-RNA total mass concentration, tau114/RNA is expected to experience stronger tau-RNA Coulombic attraction, which may counteract the entropy penalty effect of the shorter chain.Sequence-specific tau-RNA interaction is another plausible explanation. Experimental results have shown the 4R domain (K18) has a stronger binding affinity to RNA compared to full-length tau (2N4R) . Since tau114 is the N-terminal of tau187, largely corresponding to the 4R domain, it is possible that tau114 might experience stronger binding with RNA compared to tau187. However, experimentally, it is difficult to disentangle electrostatic Coulombic attractions from other enthalpic interactions. For simplicity we have not included any additional tau-RNA interactions into our theoretical and computational studies and model, but this could be included in the future, either in the mean-field (FH-VO) or in the FTS model through a Flory-Huggins interaction term acting between tau and RNA. We are currently developing such a model.To address these comments, we have added the following:cp than tau187-RNA CC, suggesting it is more favorable to phase separation. […] Additional short-ranged sequence-specific interactions between tau114 and RNA that are not present in tau187 is another possibility that is not considered in the present model.”“Comparison of the two constructs shows that tau114-RNA CC has a lower T3) In the authors' treatment using Flory-Huggin-Voorn-Overbeek , a temperature-dependent Flory chi parameter  is used to fit the data and to produce the experimental observation that the tau-RNA LLPS tendency increases with increasing temperature . This phenomenon, which is also observed for elastin, is similar to cold denaturation of globular proteins, and its possible physical origin has been discussed recently in the context of biomolecular LLPS by Lin et al., 2018; see Figure 2C of this ref] in terms of the expected temperature-dependence of hydrophobic interactions. The strength of effective attraction between nonpolar groups tends to increase with increasing temperature, as has been demonstrated from model compound transfer experiments, a fact that has long been applied to theory of protein conformational transitions, see, e.g., Dill, Alonso and Hutchinson, 1989. The same trend has also been seen in explicit-water simulations of potentials of mean force of nonpolar solutes in water, see, e.g., Figure 4 in Dias and Chan, 2014. Beyond introducing the temperature-dependent chi parameter as a mere necessity for fitting data, it would be very helpful to the readers to include this perspective (and the above-cited references) in the authors' Discussion to provide a plausible physical rationalization for the observed trend.We think the comparison of tau-RNA LLPS with cold denaturation of globular protein is inspiring. The contribution of effective attractions between nonpolar groups to the driving force in the tau-RNA LLPS process is very worthy of study. While the explicit testing of these effects is outside the scope of our paper, we have included such discussion in the main text and cited the above references. These new references are now Lin et al., 2018; Dias and Chan, 2014; Dill et al., 1989 in the main text.“A temperature dependence of χ in the form of Equation 1 (consistent with the observed LCST), can originate from hydrophobic interactions between non-polar groups, whose interaction strength tends to increase with temperature . This explanation has also been used to describe cold denaturation of proteins .”4) The statement "high crowding reagents (acting to lower the excluded volume)" is problematic. What do the authors mean by the statement? Could the current formulation of this statement be related to the aforementioned somewhat confused concept about excluded volume presented in the manuscript? Would it make more sense to say that crowding agents act to lower the chain configuration entropy of the dilute phase?In explicit solvent, repulsive interactions between protein segments and crowding agents would lead to chain compaction in the dilute phase relative to a chain in pure solvent, in the absence of the crowding agent. Thus, on average, polymer chains will become more compact with increased crowding agents as the reviewer suggests. However, our model is an implicit solvent model, meaning that the solvent degrees of freedom are not explicitly included in the statistical mechanical description. The excluded volume potential is a pair potential of mean force, and is state dependent. As shown in , mapping the explicit-crowder case onto an equivalent implicit-solvent case means that the solvent quality must change with increasing concentration of the crowders. This idea dates back to Flory’s ideality hypothesis and the effect of the crowding agents is to screen the bare excluded volume repulsion between protein segments. The effect of added crowding agents can be implicitly modeled by having an excluded volume strength parameter that decreases with increased concentration of crowding agents, implicitly accounting for this screening. This was what we meant by the statement “high crowding reagents (acting to lower the excluded volume).” The excluded volume parameter is a repulsive energy/volume between protein segments. This repulsive term causes segments to repel each other in order to maximize their exposure to the solvent in good solvent conditions. By lowering this repulsive strength parameter we are modeling the poorer solvent conditions such that the protein segments no longer swell as much as they would in the absence of crowding agents.We have changed the manuscript to read:“high crowding reagents )”.5) In the field theoretic treatment (subsection “Field theoretic simulations of a coarse-grained model of tau-RNA complex coacervation”), the temperature dependent attraction in the form of (1-theta/T) is presented as a temperature-dependent excluded-volume potential. Although it may be mathematically convenient to introduce temperature dependence as a modified "excluded volume" term, doing so is physically inaccurate and conceptually misleading. Excluded-volume interaction is a hard-core repulsion. Inter-chemical-group attractive interactions such as hydrophobic effect do not abolish the hard cores of the atoms. They just affect the potential energy landscape close to but beyond the hard cores. With this in mind, while the usage of a single Gaussian function for this "excluded volume" energy may be a mathematical simplification that is necessary for field theoretic formulations [the Γ(r) function in the last paragraph of the subsection “Coarse grained polyelectrolyte model used in FTS”], it is important to recognize that it is not entirely physically accurate . The reason is that in the authors' set up the molecular hard cores can be penetrated but in reality they cannot, and this can have consequences on the predictions of volume fractions, etc. Casting the solvent-mediated attraction as a reduction in excluded volume is also conceptually inconsistent with FH-VO because the FH excluded volume is approximately accounted for by the mixing entropy term, and the introduction of the chi parameter does not envision penetrating any excluded volume. Although this point is largely conceptual in the present context and the reviewers do not suggest redoing the field theory simulation using a more realistic excluded volume potential, it is still importance to have conceptual clarity in the authors' Discussion so as to avoid, e.g., oxymoronic statements such as “the excluded volume becomes attractive”. With a recognition of this limitation, the authors may also wish to qualify their statement regarding the "approximation-free" nature of their simulations .not imply as the review suggests that we “abolish the hard core-atoms,” but is a limiting case where the net interactions between the solvent molecules (not represented) and polymer chains  exactly counter-act each other. This is the so-called theta temperature. The temperature dependence of the “effective” excluded volume parameter is well-documented and dates back to Flory. See for example reference (2) or (3).The temperature dependence of the excluded volume potential originates from our use of an implicit solvent model; therefore, the excluded volume potential is a potential of mean force, and thus is state-dependent (including on temperature). The potential represents in an average way the effect of the solvent on the chain configurations, and was introduced by Flory to account for chain swelling of a polymer in solution. For example, setting the excluded volume to zero does always be temperature dependent. A similar situation occurs in the Chandler-Weeks-Andersen (CWA) liquid-state theory when the effective hard-sphere diameter is found by equating the compressibility of a reference system to the compressibility of a hard sphere system, giving an effective hard sphere diameter which is both temperature and density dependent.This temperature-dependent excluded volume has nothing to do with the Gaussian-smearing, and the result would be the same when using a delta-function for the excluded volume term. In general, for an implicit solvent model, where we have integrated out the solvent degrees of freedom, the excluded volume will increases the system approaches the theta condition, which is in agreement with experimental observation (4). Thus, it is not “oxymoronic” to state that the “excluded volume becomes attractive.” The conditions under which the potential becomes attractive , indicate the presence of effective attractive forces between protein segments that lead to chain collapse due to the preference of solvent-solvent interactions over solvent-monomer interactions. This would be in agreement with the large literature on hydrophobic collapse, which can be presented as a function of the solvent quality.In our particular case, the repulsive strength decreases as temperature increases. This is simply stating that as the temperature To explain this temperature dependence explicitly, we have included the following:v can be related to the residue-residue non-Coulombic interaction potential U(r) as“The excluded volume parameter ν=∫1-e-UrkBTd3rand is …”In line with the reviewer’s suggestion, we qualify our statements regarding the approximation-free nature of the simulations. They are exact with regard to the polymer physics model, but the model is itself a coarse-grained simplification of the real-system.We have replaced “serves as an approximation free method” with“FTS is a numerical approach that allows one to fully account for fluctuations, and thus to compute equilibrium properties from a suitably chosen coarse-grained representation of the true system without the need for analytical approximation.”And we have changed “approximation-free” to “complete.”6) Discussion, third paragraph, schematic equation: It would be much clearer to view the overall attractive interactions among tau proteins and RNA as an "effective free energy of interaction" that contains both enthalpic and entropic parts (wherein the entropic contribution comes largely from solvent entropy), and this effective interaction free energy  is then balanced by configurational/conformational entropy of the chains . The authors may wish to adopt such a narrative.We thank the reviewer for proposing the viewpoint of “effective free energy of interaction”. In fact, the equation does include such a narrative (see below).ΔGCC=ΔHtau/RNA⏟(-)-TΔScomb⏟(-)+ΔHex-TΔSnoncomb⏟excludedvolumeorχIn this equation, ΔGCCis the effective free energy of tau-RNA liquid-liquid phase separation . ΔHtau/RNAis the enthalpic contribution resulting from attractive interactions among tau proteins and RNA, corresponding to the enthalpy change in protein folding. TΔScombis the combinatoric entropy part, which in the FH-VO approximation is given by the classical mixing term .Our results showed that only using the first two terms can NOT capture the temperature-dependent phase separation behavior of tau-RNA (i.e. LCST). Therefore, we introduce the additional enthalpic and entropic terms ΔHex and TΔSnoncombin order to capture the non-ideal behavior.7) More discussions about controls relevant to their FTS simulations is needed to evaluate the FTS methodology.The idea of describing a polymer chain in solution with functional integrals dates back to the pioneering work of Edwards, de Gennes, and Helfand in the 1970s. We employ a numerical technique called Complex-Langevin (CL) sampling to solve for the ensemble-averaged observables. This technique (see (5)) has been used to study synthetic polymer systems in dozens of papers. In fact, FTS via CL sampling was introduced ten years ago to study polyelectrolyte complex coacervation, although the full phase diagrams could not be obtained yet at that time .More recent work provides the first phase diagram from FTS via CL sampling for simple polyelectrolytes in 2017 . This work can be seen as the benchmark of our current implementation. The novelty here lies in applying this technique that is well-established in the polymer community  to a biological system, namely of tau-RNA CC, and mimicking biological conditions.We have added the following to the main text:“Field theoretic simulation (FTS) has been widely used to model synthetic polymers , including LLPS in polyelectrolytes and polyampholytes . […] The main advantage of this approach is that it does not rely on analytical approximations, and thus can be useful when comparing directly to experiment at conditions where such approximate theories break down”B) parameter obtained from Equation 3 (there is no Equation 3 in the manuscript)? How Equation 2 can be used to obtain lB if the relative dielectric constant term still needs to be determined? This is a very important section of the paper and a more detailed discussion is needed here.8) When the authors compared simulation to experiment (subsection “Comparison between simulation and experiment”), how was the Bjerrum length  was then calculated according to Equation 2. The reviewer is correct that in reality the dielectric constant is temperature dependent. For example, from , the dielectric constant of water at 10 °C is 83.8 and at 30 °C is 76.5. These values will change yet again slightly in different buffer conditions. We have ignored theses complexities and estimated εr as ~ 80. Our results are best interpreted as showing the relative contributions from the electrostatics vs. the excluded volume effects, both of which are important. One could choose a more detailed parameterization of the FTS simulations, but since the excluded volume parameter is unknown and important, the quality of the results would be similar.We thank the reviewer for pointing out this important point. Comparison to experiment requires estimating a value for the parameters in the polymer model. Given the “coarseness” of the model and for simplicity, we have set εWe have added the following statement:r will depend on temperature, for simplicity we treat this parameteras a constant such that the Bjerrum length Bl~ 1/T. Thus, Bl can be estimated at the experimental cloud point temperature directly from Equation 2 , which leaves only one unknown parameter v.”“Although ε9) In the conclusion, the authors state that the simulation provides "knowledge that can be used to design experiments that perturb this parameter space in vivo". It would be extremely useful if the authors test FTS and their results by considering tau phosphorylation. Even just modeling tau phosphorylation without RNA would be useful. Additional discussion along this line, if attainable in a timely fashion, is preferred, but not required by the reviewers and the editors.The topic of post-translational modification and phosphorylation affecting liquid-liquid phase separation is what we are currently investigating. However, given the interest in how phosphorylation might affect tau-RNA CC, we have included a brief preview in the main text which is copied here:“To demonstrate that FTS of tau-RNA LLPS can be applied to consider the effect of post translational charge modification of tau, we tested FTS of phosphorylation as an example. […] In full-length tau, most of the phosphorylated sites are not in the positively charged repeat domain region, indicating that its LLPS might follow the same principles as the self-coacervation seen in polyampholytes .”B is plotted in Figure 6A. Could the authors confirm in the figure legend that this is just a plot of Equation 2 and no temperature dependence of the relative permittivity εr was introduced in Figure 6A?10) In Figure 6C, for the 120mM NaCl data, the simulated transition temperatures (open green squares) have a significantly higher rate of decrease with respect to increasing [tau] than the corresponding experimental data (blue circles). Could the authors provide a plausible reason for this discrepancy? The Bjerrum length lWe confirm as stated above that no explicit temperature dependence was included in the relative permittivity, and yes, Figure 6A is the strictly linear plot according to Equation 2 with ϵr=80 plotted for clarity, whereas Figure 6B is a linear fit to the data to determine the excluded volume repulsive potential parameter as a function of temperature. This is made clearer in the first paragraph of the subsection “Comparison between simulation and experiment”.We find it difficult to infer with confidence a difference in the rate of decrease between experiment and simulation, given the limited experimental data (3 data points). Nonetheless, we find it promising and quite remarkable that without introducing any new fit parameters, the simulation  and the experiment agree reasonably well in estimating the dilute phase concentration and how it changes with salt concentration. This is a positive result, and not something that one would expect using a more approximate theory.References:1) Aumiller and Keating, Nature Chemistry, 8, 2016, p. 1292) Rubinstein and Colby, Polymer Physics, Oxford University Press, Section 3.3.33) Gert Strobl, The Physics of Polymers, 3rd ed., Springer, Section 2.34) Bianconi, Ciasca, Tenenbaum, Battisti, Campi, J Biol Phys. 2012 Jan 1;38(1):169–795) Fredrickson, Ganesan, and Drolet,Macromolecules 2002, 35, 16–39


Betanodaviruses have bi-segmented positive-sense RNA genomes, consisting of RNAs 1 and 2. For some members of the related genus alphanodavirus, the 3′ terminal 50 nucleotides (nt) of RNA2, including a predicted stem-loop structure (3′SL), are essential for replication. We investigate the possible existence and role of a similar structure in a reassortant betanodavirus strain (RGNNV/SJNNV). In this study, we developed three recombinant strains containing nucleotide changes at positions 1408 and 1412. Predictive models showed stem-loop structures involving nt 1398–1421 of the natural reassortant whereas this structure is modified in the recombinant viruses harbouring point mutations r1408 and r1408–1412, but not in r1412. Results obtained from infectivity assays showed differences between the reference strains and the mutants in both RNA1 and RNA2 synthesis. Moreover, an imbalance between the synthesis of both segments was demonstrated, mainly with the double mutant. All these results suggest an interaction between RNA1 and the 3′ non-coding regions (3′NCR) of RNA2. In addition, the significant attenuation of the virulence for Senegalese sole and the delayed replication of r1408–1412 in brain tissues may point to an interaction of RNA2 with host cellular proteins. Nodaviridae, which also includes alphanodaviruses that naturally infect insects [Betanodaviruses, the causative agents of a highly infectious fish disease known as viral nervous necrosis (VNN), are members of the family  insects .Betanodaviruses are small, non-enveloped icosahedral viruses with bipartite positive-sense genomes. The larger genome segment, RNA1 (3.1 Kb), encodes the viral RNA-dependent RNA polymerase (RdRp), also known as protein A, while the smaller genome segment, RNA2 (1.4 Kb), encodes the only structural protein. RdRp replicates both genomic RNAs and also transcribes RNA3, a non-encapsidated subgenomic RNA that is co-terminal with the 3′ end of RNA1 and encodes B2 protein, an inhibitor of cell RNA silencing, and B1, an anti-necrotic death factor Solea senegalensis), gilthead sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax) [Betanodavirus isolates are currently classified into four different genotypes: striped jack nervous necrosis virus (SJNNV)-type, tiger puffer nervous necrosis virus (TPNNV)-type, barfin flounder nervous necrosis virus (BFNNV)-type and redspotted grouper nervous necrosis virus (RGNNV)-type, on the basis of phylogenetic analysis of the RNA2 variable region . However labrax) . Reassor labrax) . To date labrax) , which sreasy grouper nervous necrosis virus (GGNNV) [Genome replication is a critical step in virus life cycles. Positive-strand RNA viruses replicate their genomes by viral replicase complexes consisting of virus-encoded RNA-dependent RNA polymerase and other proteins  . The rep (GGNNV) .in vitro, as well as on virulence for fish, has been analysed.In the present report, we performed RNA2 structure predictions to investigate the possible existence of 3′SL structures in reassortant betanodavirus strains (RGNNV/SJNNV) isolated from sole, which show differences with the SJNNV-type reference strain SJNag93 at nucleotides 1408 and 1412 [For the natural reassortant betanodavirus strain wt160 (wild-type strain), the software predicted a pseudo-knot in the 3′ termini followed by a stem-loop structure involving nt 1398–1421 . This stin vitro, RNA synthesis and viral production were studied in E-11 cells.To investigate whether the point mutations in the 3′NCR of the RNA2 segment influence the efficiency of viral replication 4 to 4.8×105 copy number ml−1 and from 1 to 6 h p.i., a slight increase was observed in the reference strains  was observed in the wt strain, whereas in the recombinant strains the beginning of the exponential phase was delayed by up to 6 h (Table S1). The ratio of positive- to negative-sense RNA ranged from 5 : 1 at 1 h p.i. to 15 : 1 at 48 h p.i. in wt160, and from 3 : 1 to 23 : 1 in the mutant strains  and negative strand generated as an intermediate during the RNA2 replication process. Sense and anti-sense RNA transcripts were used to assess the strand specificity of the method. Neither false priming in the RT nor cross-amplification was detected. Amplification of the wrong strand was checked by carrying out the negative/positive-strand RNA quantitation protocol in the presence of both strand-synthetic RNAs. As with RNA1, RNA2 was detected as early as 1 h p.i. , reachin at 48 h ; and, as strains .No significant differences (below 1 log of titre) were observed among the different strains. Comparison of the production of both genomic segments showed that the number of RNA2 copies was higher than those of RNA1 at all time points analysed. It is worth noting that the differences observed were higher in the mutant strains, i.e. at the different time points the ratio of RNA2 to RNA1 ranged from 20 : 1 to 189 : 1 in wt160, whereas in r1408–1412 it ranged from 40 : 1 to 815 : 1.2>0.95).However, we needed to be more precise since, on the one hand, we were aiming to determine whether there were significant differences among reference strains and mutants, and on the other to demonstrate that there was a balance between the reference strains kinetics synthesis of RNA1 and RNA2, and of RNA2(+) and RNA2(−), and whether this balance was destroyed in any of the mutants . TherefoP>0.05); only in the case of wt160 were differences between both slightly significant (P=0.0475) (Table S2). When the analysis was extended to RNA1/RNA2, a correlation between both segments was observed in the reference strains (wt160 and r160), and a significant lack of correlation was demonstrated in two of the mutants: r1408 and r1408–1412.Comparison between RNA2(+) and RNA2(−) curves revealed that in most cases no correlation could be determined (No differences in RNA1 synthesis were observed among the strains (Table S3). However, significant differences in RNA2(+) existed between the reference strains and mutants r1412 and r1408–1412.2<0.95) adjusted, either to a linear or a second-degree polynomial function , they could not be statistically compared to confirm those differences or similarities.Culture supernatants harvested at 6, 15 and 24 h p.i. yielded no cytopathic effect when inoculated into E-11 cells; therefore, to assess viral production and construct the growth curve, the study was initiated at 48 h p.i. As observed in 4 copies ml−1  and no differences were observed among the strains. However, on subsequent days as extracellular RNA1 increased, slight differences (<0.5 log) were detected between the reference and mutant strains (8 copies ml−1). Comparison of RNA1 production kinetics revealed no significant differences among the strains .Viral production was also investigated in terms of extracellular viral RNA . Detailed analysis indicated that the double mutant and r160 showed slightly lower production than the other strains at 48 h p.i. Subsequently, all strains displayed very similar values and a difference of around 0.5 log was observed between the reference strains and r1408 only at 6 days p.i. However, when the RNA2 production curves were analysed, significant differences were observed between those corresponding to the reference strains and r1408 and r1412.Extracellular RNA2 reached higher values than RNA1, copy numbers ranging from 5.9×10In the experimental challenges performed by immersion, 95 % mortality was observed at 30 days p.i. in the groups infected with wt160. Survival percentage increased in sole infected with different recombinants harbouring point mutations, especially in those groups infected with the double mutant. In fish infected with recombinants harbouring one single mutation (r1408 and r1412) survival was around 40 %, whereas in the groups infected with the double mutant r1408–1412 the survival rate increased up to 68 % . Mortali3 to 2.2×105 copies g−1 at 96 h p.i., reaching a higher viral load than mutants at 96 h p.i. (P<0.05), whereas RNA1 yield in the mutants showed a moderate increase at the beginning but reached a plateau after 48 h for r1408, and after 72 h for r1408–1412.All strains were detected in brain tissue at 24 h p.i. with a similar RNA1 copy number . The RNA5 copies g−1 at 24 h p.i. to 7.0×106 copies g−1 at 96 h p.i. (P<0.05).RNA2 quantification revealed a similar picture, the copy number in the wt strain increasing steadily from 5.4×106 h p.i. . HoweverIt has been demonstrated that the 3′ non-coding region of positive-strand viral RNAs is involved in RNA synthesis with different potential roles, namely, in minus strand synthesis (promotion or regulation of access to minus strand origin), in the modulation of translation or in RNA targeting to specific subcellular sites .cis-acting replication signal capable of directing the replication of this segment. In experiments performed with Flock house virus (FHV) and Nodamura virus (NoV), replacement or deletion of this cis-acting signal has demonstrated that it is RNA3-dependent and includes a 3′SL structure, respectively [In the present study, a stem-loop structure was predicted at the 3′ termini of RNA2 of reassortant betanodavirus strain wt160 comprising nt 1398–1421, which is in agreement with previous predictive approaches for the SJNNV strain . In alphectively . In addiectively . AlthougP<0.0001). Interestingly, this was the strain yielding the lowest mortality in vivo, which may indicate that disruption of the 3′SL structure affects its survival in fish by affecting its replication. However, the fact that both single mutants also showed reduced mortality and that r1412 showed no apparent alteration in 3′SL suggests that minor changes in 3′NCR have important effects on virulence in vivo.With regard to RNA1, although no significant differences in its kinetics of synthesis were observed among the strains, differences of more than 1 log between the reference strains and both recombinants harbouring point mutations (r1408 and the double mutant r1408–1412) were noted at 24 h p.i. Furthermore, in these strains the ratio RNA2:RNA1 was up to sixfold higher than in wt160 or r160, and an imbalance between the synthesis of both segments was demonstrated, mainly with the double mutant  and persistently infected with a C-type retrovirus (SnRV) [−1 streptomycin (100 mg ml−1) and 2 % fetal bovine serum , at 25 °C. After each experiment the identity of viral recombinants was conﬁrmed by 3′ RACE (System for Rapid Amplification of cDNA Ends) as described in [In the present study, two betanodavirus strains were used as reference: the natural reassortant with an RGNNV RNA1 and an SJNNV RNA2 SpSsIAusc16003 (abbreviated to wt160)  and the s (SnRV) . Cells wribed in  and sequ50) following the methodology described in [For viral titration, the endpoint dilution method was performed in 96-well plates (Sarstedt), and the titres expressed as the viral dilution infecting 50 % of the cell cultures , penicillin (100 U ml−1) and streptomycin (100 mg ml−1) at 37 °C in a 5 % CO2 humidified chamber. Geneticin  was added every two subcultures.BSRT7/5 cells , kindly cACAATACTCGGCGGGGTC GGCATGGC3′, Mut1412- 5′GCTGATCGCTACAtTACTC GGCGGGGTCGGCATGGC3′ and Mut1408-1412- 5′GC TGATCGCcACAtTACTCGGCGGGGTCGGCATGGC3′.The strains r1408, r1412 and r1408–1412 – recombinants with RNA1 identical to wt160 and a wt160 RNA2 with point mutations – were generated by reverse genetics as described previously [The resultant plasmids contained nucleotide changes (T1408C and A1412T) in the 3′ NCR of RNA2 . BSRT7/5 were transfected, as previously described in detail [RNA structure predictions were performed on the terminal 100 nucleotides of the 3′ end of RNA2 of each strain, using HotKnots 2.0  and MfolTo study the effect of the mutations on viral replication, two different assays were carried out in E-11 cells comparing the replication kinetics of all recombinant viruses to the wild-type reassortant wt160. At the end of each experiment the maintenance of mutations in the recombinant strains was confirmed by sequencing.2>0.95), the regression analysis was performed from the second time point. When no significant regression line was found, Bonferroni’s multiple comparison was performed between consecutive time points to determine the existence of a significant increase in RNA load.The genome synthesis at early infection (1–48 h p.i.) was determined by inoculating cells in 48-well plates (Sarstedt) with each viral strain. The cells were infected at a m.o.i. of 0.01 and subsequently washed, overlain with fresh medium and incubated at 25 °C. At 6, 15, 24 and 48 h p.i. supernatants from three wells per strain were removed and the cells were scratched, suspended in 0.5 ml of L-15 medium and harvested separately. Quantitative real-time PCR (qPCR) was used to determine the number of genome copies from cell lysates at the different time points, as further indicated. Extension of the exponential phase was established using regression lines and/or significant increase in RNA levels. First, we assessed whether the growth of each strain adjusted to a line, using regression analysis from the first time point to the last; if the resultant regression line was not significant (R2 (Sarstedt) flasks were infected with the various vir at m.o.i. of 0.01. After 1 h adsorption, the inoculum was removed and the monolayers were washed three times and covered with L-15. The infected cells were incubated at 22 °C and monitored daily for cytopathic signs. Samples from culture supernatants were harvested at 6, 15, 24 and 48 h p.i., and then on a daily basis until the cytopathic effect was extensive. All samples were stored at −80 °C until use. Infectious virus and viral RNA were quantified by end-point titration in E-11 cells and RT-qPCR, respectively.To study viral production, 25 cmad libitum daily. All efforts were made to minimize animal suffering. The fish were placed in opaque tanks containing sea water and acclimated at the experimental temperature (22 °C) for 10 days. Before experimental infection, 10 fish were sacrificed with an anesthetic overdose  and used for both the diagnosis of bacterial pathogens and four regular viral agents: infectious pancreatic necrosis virus (IPNV), infectious haematopoietic necrosis virus (IHNV), viral haemorrhagic septicaemia virus (VHSV) and betanodavirus, as described in [Experimental infections in Senegalese sole were performed to assess the virulence of the various recombinant strains. Juvenile sole (mean weight 2 g) were obtained from a commercial fish farm and maintained at the fish facilities of the Universidade de Santiago de Compostela. The fish were fed dry commercial pellets ribed in .5 TCID50/ml for 3 h, with strong aeration. Control fish (n=10) were handled as for the infected groups and L-15 medium was used for mock infection. Mortality and clinical signs were recorded daily, and dead fish were removed. Both sampled and surviving fish were euthanized using an overdose of MS-222. Brain tissue (in pools of five) were analysed by cell culture and RNA quantification.Groups of 30 juvenile sole were infected with each of the three mutants and also with the wild strain (wt160), in triplicate. The virulence of r160 has previously been shown to be identical to that of wt160 , and theThree further groups of 24 sole were placed in three separate tanks and infected with mutants r1408 and r1408–1412, and with the wild strain (wt160), under the conditions described above. A control group of 12 fish was also included. Six fish from each tank were sampled at 24, 48, 72 and 96 h p.i. and brain tissues were analysed individually by RT-qPCR to quantify the level of viral RNA synthesis as described below.−1 penicillin, 1000 µg ml−1 streptomycin, 500 µg ml−1 gentamycin and 500 µg ml−1 partricin). The samples were centrifuged at 3000 g for 15 min at 4 °C. When tissue pools where used, the resulting supernatants were split into two aliquots: one stored at −80 °C for later use in RT-qPCR and the other used at that time for cell culture.Brain tissues were homogenized in Earle's balanced salt solution  supplemented with antibiotics  for 7 days.Cell lysates from the in vitro experiments and from tissue homogenates using the EZNA Total RNA I kit (Omega Biotek) in accordance with the supplier’s protocol. Extracted RNA was reverse transcribed with Superscript IV reverse transcriptase (Invitrogen) using random primers. For qPCR, reactions were processed with 2 µl of cDNA samples in a final volume of 20 µl, using iQTMSYBRGreenSupermix (Bio-Rad) and 200 nM of primers SnodR1 F/R [1–107 copies µl−1. Reactions were carried out using a CFX96TM Real-Time PCR Detection System (Bio-Rad). Following an initial denaturation/activation step at 95 °C for 15 min, the mixture was subjected to 40 cycles of amplification . Data were expressed as mean±sd of copies per millilitre of cell lysate or millilitre of supernatant or grams of brain tissue for RNA synthesis, viral production and in vivo experiments, respectively.Total RNA was extracted from the samples obtained in the odR1 F/R  for RNA1in vitro. A 427 pb-long region from RNA2 of the w160 strain was recovered by RT-PCR by using F2R3 primers [in vitro transcription reactions using the MAXIscript SP6/T7 Transcription Kit (Invitrogen) according to the manufacturer’s instructions. To synthesize the positive-sense template, pR2f2r3 was linearized with PstI enzyme. The RNA transcripts were synthesized using T7 promoter. To synthesize the negative-sense template, pR2f2r3 was linearized with SacII enzyme and the in vitro transcripts were produced using the promoter SP6. For each reaction, 5 µl of linearized DNA template was incubated at 37 °C for 1 h with RNA polymerase. The transcribed RNA was digested with Turbo DNAase (Ambion) according to the manufacturer’s protocol. The RNA transcripts were further purified by extraction with a High Pure RNA isolation kit (Roche) with an on-column DNase-treatment step following the manufacture’s protocol. The quality and quantity of the RNA transcripts were measured using a NanoDrop ND-100 spectrophotometer (NanoDrop Technology).The tagged primers TAGnodaR2Rv (5′ CAGGCGTTGTCCGTGTTCTAATCAATGGGCAACGGTTTGTC 3′) and TAGnodaR2Fw (5′ CAGGCGTTGTCCGTGTTCTGGATTTCGTTCCATTCTCTTGGG 3′) were used for positive- and negative-sense RNA2 cDNA synthesis, respectively. Before qPCR, the reactions were incubated with 2 U of exonuclease at 37 °C for 30 min followed by 70 °C for 15 min as previously described . Real-ti primers  and inse7 copies.The efficiency of qRT-PCR using T7- and SP6-transcribed RNAs, positive and negative strand, respectively, was evaluated using the slope of the amplified curve to determine the copy number of the RNA2 region starting at 5×10P<0.05 was considered statistically significant. P-values were adjusted by the Bonferroni method for multiple comparison. In the case of linear regression, to determine whether two or more curves were similar, the slopes and intersections were compared by an F-test. In the case of kinetics adjusted to second-degree polynomial functions (y = B2x2+B1x+B0), the parameters B1 and B2 were chosen for comparison: B1 giving the position of the parabola on the x-axis and B2 the amplitude of the parabola, and the  position of its vertex. When the P-values were low (P<0.05) for both parameters, the theory that differences are due to random sampling was rejected and therefore both curves were considered different; if differences were demonstrated only for the intersections, the curves were assumed to be distinct but parallel.Statistical analyses were carried out by GraphPad Prism version 6.00 . Click here for additional data file.


Exploring virus–host interactions is key to understand mechanisms regulating the viral replicative cycle and any pathological outcomes associated with infection. Whereas interactions at the protein level are well explored, RNA interactions are less so. Novel sequencing methodologies have helped uncover the importance of RNA–protein and RNA–RNA interactions during infection. In addition to messenger RNAs (mRNAs), mammalian cells express a great number of regulatory non-coding RNAs, some of which are crucial for regulation of the immune system whereas others are utilized by viruses. It is thus becoming increasingly clear that RNA interactions play important roles for both sides in the arms race between virus and host. With the emerging field of RNA therapeutics, such interactions are promising antiviral targets. In this review, we discuss direct and indirect RNA interactions occurring between RNA viruses or retroviruses and host non-coding transcripts upon infection. In addition, we review RNA virus derived non-coding RNAs affecting immunological and metabolic pathways of the host cell typically to provide an advantage to the virus. The relatively few known examples of virus–host RNA interactions suggest that many more await discovery. Viruses are likely the most abundant biological entity and the known global virosphere is greatly expanding ,2. ViralHerpesviridae) or single-stranded  genomes; RNA viruses have double-stranded , or single-stranded positive sense  or negative sense  genomes, where orientation is defined for coding mRNA sequence to be of positive polarity. In addition, Hepadnaviridae are DNA viruses with RNA intermediates, and Retroviridae are (+)RNA viruses with DNA intermediates . U. U87]. U365 . . 164]. ICulex pipiens accumulate in midgut cells and display RNAi suppressor activity by counteracting Dicer. This process modulates transmission of the virus to the salivary glands and thereby its transmission to the next host [-/--IRF7-/- or IFNAR-/- mice with sfRNA defective WNV strains demonstrated that sfRNA production is critical to sustain infection in animals with intact interferon response [The biological functions of sfRNAs vary between organisms infected B. WNV deext host ,173. In ext host ,167,174.ext host . In addiresponse . An alteWhereas viral lncRNAs are well described for large DNA viruses like the PAN RNA expressed by Kaposi’s sarcoma-associated herpesvirus (KSHV) , lncRNAsIn recent years, there has been an increasing interest in developing tools to systematically dissect the biological significance of RNA interactions governing cellular processes as well as pathogen–host interactions. These novel methodologies are expanding our knowledge on gene expression regulation mediated by ncRNAs. It is also becoming clear that such mechanisms play central roles in the regulation of the cellular response to viral infection. Direct binding to cellular ncRNAs is further exploited by RNA viruses to their own advantage, and this could provide new modalities for therapy. Thus, virus–host RNA interactions are becoming a novel focus area to fully understand the complexity of the virus life cycle, but more systematic investigation is required. The role of cellular and viral-derived small RNA is further along compared to lncRNAs and other RNA species. Despite such progress, much remains to be investigated to understand which virus–host RNA interactions are the result of the cell attempting to clear the infection and which represent a viral strategy to create a favorable host cell environment. Lastly, the often narrow focus on pathogenic viral species suggests that a broader investigation of the RNA virosphere will reveal exciting discoveries in this emerging field.Unanswered questionsAre there undiscovered cases of RNA viruses benefiting from miRNA-mediated repression of replication in certain tissues?What undiscovered examples of direct viral RNA-miRNA interactions exist and what are their functions?Does virus-mediated RNA sequestration have functional consequences for pathology or immune responses?Is miRNA manipulation, even for direct use in genome replication, a feature of persistent viruses?Does non-coding RNA in post-transcriptional regulation play unappreciated roles during chronic infection?What other types of cellular RNA interact directly with viral RNA?Do other mammalian RNA viruses accumulate functional non-coding RNA species akin to sfRNAs?


Yersinia includes three human pathogenic species, Yersinia pestis, the causative agent of the bubonic and pneumonic plague, and enteric pathogens Y. enterocolitica and Y. pseudotuberculosis that cause a number of gut-associated diseases. Over the past years a large repertoire of RNA-based regulatory systems has been discovered in these pathogens using different RNA-seq based approaches. Among them are several conserved or species-specific RNA-binding proteins, regulatory and sensory RNAs as well as various RNA-degrading enzymes. Many of them were shown to control the expression of important virulence-relevant factors and have a very strong impact on Yersinia virulence. The precise targets, the molecular mechanism and their role for Yersinia pathogenicity is only known for a small subset of identified genus- or species-specific RNA-based control elements. However, the ongoing development of new RNA-seq based methods and data analysis methods to investigate the synthesis, composition, translation, decay, and modification of RNAs in the bacterial cell will help us to generate a more comprehensive view of Yersinia RNA biology in the near future.The genus  This showed that RNA-based gene expression control is highly complex and global, and almost as diverse and multifarious as transcriptional control. Multiple structured RNA elements have been identified in the 5′-untranslated regions of mRNAs sensing temperatures (RNA thermometers) or metabolites (riboswitches) in order to modulate mRNA translation and/or stability , i.e., ranging from enteritis, watery diarrhea, mesenteric lymphadenitis to post-infectious extraintestinal sequelae  and the anti-phagocytic Yop effector proteins (Yops) to prevent the phagocytic attack. In addition, the bacteria induce high-affine ion (Fe2+ and Zn2+) sequestration systems, diverse stress response, and nutrient uptake systems to counteract ion deprivation, radical/oxidative stress, anti-microbial peptides, and nutrient restraints  expressed by some Y. pseudotuberculosis strains, e.g., YPIII and IP2666, is one of these factors. Suppression of CNFY triggers interferon-γ mediated responses fostering non-inflammatory bactericidal activities. Moreover, it promotes Ido1- and Arg1-mediated Tregs activation and proliferation and other immune suppressing and tolerogenic mechanisms that avoid systemic inflammation, prevent tissue destruction and drive this Y. pseudotuberculosis strain into persistency  are already induced in the acute phase. It is assumed that this priming gives the bacteria a fitness edge against the host immune defense and facilitates establishment of a persistent microbiota-type life style  was observed during the establishment of persistence, whereas genes encoding important components of the T3SS and the Yops were down-regulated. This is accompanied by a change in the expression of the transcription factors of the Crp-CsrA-RovM-RovA regulatory cascade, indicating that these control factors determine the expression switch  of virulence, stress adaptation and metabolic genes in Y. pseudotuberculosis  similar to mgtA of Salmonella . Based on the whole-genome transcriptomics study of lcrF (Table lcrF RNA thermometer is highly conserved between the human pathogenic Yersinia species and consists of a two stem-loop structure of which the second hairpin sequesters and hides the ribosome binding site (RBS) within its stem region at moderate temperatures (20–30°C). Elevated temperatures (37°C) lead to partial unfolding of the stem-loop, which renders the RBS accessible for the small subunit of the ribosome, leading to translation initiation , and two very important virulence genes, encoding the cell adhesion and invasion factor Ail, contributing to host cell colonization and host resistance, and the CNFY toxin, triggering host tissue inflammation and damage  are central regulators that play an essential role in various physiological and virulence associated processes in bacteria   and expression of the T3SS/yop genes  and the ribosome recycling factor Rrf  , synthesis of RepA is repressed by (i) CopB, a transcriptional repressor that interacts with the repA promoter, and (ii) the asRNA CopA, that binds to the 5′ end of the longer of two repA transcripts preventing translation of a short leader peptide whose translation is coupled with and required for repA translation  that have been discovered to be part of the Yersinia biology.In summary, the identification of regulatory sRNAs and asRNAs using RNA-seq in the context of an infection process highlights that this approach is a powerful tool for the discovery of new infection-relevant regulatory processes. Application of newly developed RNA-seq technologies such as pulse-chase-expression global target searches . As previously described, this can be achieved by masking the RBS with an RNA binding protein such as CsrA  was found to contribute to post-transcriptional repression by YopD . The amount and type of RNases, their essentiality and collaboration in RNA metabolism is strongly depending on the investigated pathogen  , double-stranded endonucleases  and 3′-exonucleases     of the multitude of mRNAs, asRNAs, and sRNAs identified in ) Figure , by cova) Figure , RNA int) Figure . These t) Figure . SimilarYersinia virulence factors are: How are they conserved or remodeled during evolution between different strains and species, and how do acquired variations change virulence gene expression and pathogenicity? Numerous species- and strain-specific sensory and regulatory RNAs and many intra-species variations of certain sRNAs have been discovered in human pathogenic Yersiniae over the past years. For instance the PhoP regulator, controlling expression of the CsrC sRNA is not expressed in Y. pseudotuberculosis strain YPIII, but in IP32953  can be identified enabling a dynamic view of ribosome movement along all mRNAs of a bacterial cell at a certain growth condition (Li et al., Another future task will be to elucidate the precise molecular mechanism how the RNA-binding translocator protein YopD controls translation of Yersinia will also have to address how the different riboregulators and RNAs contribute to the colonization of different host niches and whether and how they are implicated in the reprogramming of the pathogen, e.g., Y. pseudotuberculosis during the switch from the acute to the persistent infection mode. As regulatory RNAs, such as CsrB and CsrC, are implicated in the control of the regulator RovA, which forms a bistable switch, leading to high- and low-invasive subpopulations (Nuss et al., Yersinia and will open up a new level of our understanding of virulence control of bacterial pathogens.Ongoing characterization of the identified RNA-based control mechanisms of All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.