In many important cellular processes, including mRNA translation, gene transcription, phosphotransfer, and intracellular transport, biological “particles” move along some kind of “tracks”. The motion of these particles can be modeled as a one-dimensional movement along an ordered sequence of sites. The biological particles  have volume and cannot surpass one another. In some cases, there is a preferred direction of movement along the track, but in general the movement may be bidirectional, and furthermore the particles may attach or detach from various regions along the tracks. We derive a new deterministic mathematical model for such transport phenomena that may be interpreted as a dynamic mean-field approximation of an important model from mechanical statistics called the asymmetric simple exclusion process (ASEP) with Langmuir kinetics. Using tools from the theory of monotone dynamical systems and contraction theory we show that the model admits a unique steady-state, and that every solution converges to this steady-state. Furthermore, we show that the model entrains (or phase locks) to periodic excitations in any of its forward, backward, attachment, or detachment rates. We demonstrate an application of this phenomenological transport model for analyzing ribosome drop off in mRNA translation. Movement is essential for cell function. Cargoes like organelles and vesicles must be carried between different locations in the cells. The information encoded in DNA and mRNA molecules must be decoded by “biological machines” (RNA polymerases and ribosomes) that move along these molecules sequentially.Many of these important biological transport processes are modeled as the movement of particles along an ordered chain of sites. In the context of intracellular transport, the particles are motor proteins and the chain models actin filaments or microtubules. In transcription, the particles are RNAPs moving along the DNA molecule, and in translation the particles are ribosomes moving along the mRNA molecule see .The movement in such processes may be unidirectional, as in mRNA translation elongation, or bidirectional, as in transcription or translation initiation. Indeed, the normal forward flow of the RNAP may be interrupted, due to transcription errors and various obstacles such as nucleosomes, in which case the RNAP tracks back a few nucleotides and then resumes its normal forward flow –4. TransTo increase efficiency, many particles may move simultaneously along the same track thus pipelining the production process. For example, to increase translation yield, a number of ribosomes may act simultaneously as polymerases on the same mRNA molecule , 7.The moving biological particles have volume and usually cannot overtake a particle in front of them. This means that a slowly moving particle may lead to the formation of a traffic jam behind it. For example, Leduc et al.  have stuIn some of these biological transport processes the biological machines may either attach or detach at various sites along the tracks. For example, ribosomes may detach from the mRNA molecule before reaching the stop codon due to traffic jams and ribosome-ribosome interactions or due to depletion in the concentration of tRNAs –13. AlsoDeveloping a better understanding of these dynamical biological processes by combining mathematical modeling and biological experiments will have far reaching implications to basic science in fields such as molecular evolution and functional genomics, as well as applications in synthetic biology, biotechnology, human health, and more. Mathematical or computational modeling is especially important in developing approaches for manipulating and controlling these processes, e.g. in order to optimize various goals in biotechnology.asymmetric simple exclusion process (ASEP)  describing the normalized “level of occupancy” (or density) at site i at time t, with xi(t) = 0 [xi(t) = 1] representing that site i is completely free [full] at time t. Since xi(t) ∈  for all t, it may also be interpreted as the probability that site i is occupied at time t.The MFALK is a set of The MFALK contains four sets of non-negative parameters:i, i = 0, …, n, controls the forward transition rate from site i to site i + 1,λγi, i = 0, …, n, controls the backward transition rate from site i + 1 to site i,βi, i = 1, …, n, controls the attachment rate to site i,αi, i = 1, …, n, controls the detachment rate from site i,i, γi, αi and βi has units of 1/time.where we arbitrarily refer to left-to-right flow along the chain as forward flow, and to flow in the other direction as backward flow. Each parameter λThe dynamical equations describing the MFALK are:1x1(1 − x2) represents the flow from site 1 to site 2. This increases with the occupancy in site 1, and decreases with the occupancy in site 2. In particular, this term becomes zero when x2 = 1, i.e. when site 2 is completely full. This is a “soft” version of the hard exclusion principle in ASEP: the effective entry rate into a site decreases as it becomes fuller. Note that the constant λ1 ≥ 0 describes the maximal possible transition rate from site 1 to site 2. Similarly, the term λ2x2(1 − x3) represents the flow from site 2 to site 3. The term γ2x3(1 − x2) [γ1x2(1 − x1)] represents the backward flow from site 3 to site 2 [site 2 to site 1]. Note that these terms also model soft exclusion. The term β2(1 − x2) represents attachment of particles from the environment to site 2, whereas α2x2 represents detachment of particles from site 2 to the environment  may be positive, zero, or negative.The αi = βi = γi = 0 for all i the MFALK becomes the RFM, i.e. a dynamic mean-field approximation of the unidirectional TASEP with open boundary conditions and without Langmuir kinetics.In the particular case where x denote the solution of t ≥ 0 for the initial condition x(0) = a. Since the state-variables correspond to normalized occupancy levels, we always assume that a belongs to the closed n-dimensional unit cube:Cn) denote the interior of Cn, and let ∂Cn denote the boundary of Cn. The next section analyzes the MFALK defined in Let α0 ≔ 0, γ0 ≔ 0, αn+1 ≔ 0, and βn+1 ≔ 0. Recall that all the proofs are placed in For notational convenience, let Cn is an invariant set for the dynamics of the MFALK, that is, if a ∈ Cn then x ∈ Cn for all t ≥ 0. The following result shows that a stronger property holds.It is straightforward to show that Proposition 1Suppose that at least one of the following two conditions holds:orThen for anyτ > 0 there existsd = d(τ) ∈ , but becomes reducible at some points on the boundary of Cn.This means in particular that trajectories that emanate from the boundary of L1 norm, i.e. for z|1 = |z1| + ⋯ + |zn|.Differential analysis and in particular contraction theory proved to be a powerful tool for analyzing nonlinear dynamical systems. In a contractive system, trajectories that emanate from different initial conditions contract to each other at an exponential rate –60. Let Proposition 2LetNote thatη ≤ 0. For anya, b ∈ Cnand anyt ≥ 0,a, b ∈ Cn. Define the distance between densities using the L1 distance: |a − b|1 = |a1 − b1| + ⋯ + |an − bn|, i.e. the sum of the absolute differences at each site. Consider two time evolutions of the MFALK x and x, i.e. the solution at time t when initialized with x(0) = a and with x(0) = b. Then the difference between x and x decreases with time with an exponential rate η. Thus, as time progresses the MFALK “quickly forgets” the initial condition and, as we will see below, the density always converges to the same steady-state density.η depends on the MFALK parameters and increasing all the sums αi + βi, i = 1, …, n, makes the system “more contractive”. Indeed, these parameters have a direct stabilizing effect on the dynamics of site i, whereas the other parameters affect the site indirectly via the coupling to the two adjacent sites.The value η = 0, L1 distance between trajectories does not increase. This is not strong enough to prove the asymptotic properties described in the subsections below. Indeed, in this case it is possible that the MFALK will not be contractive with respect to any fixed norm. Fortunately, a certain generalization of contraction turns out to hold in this case.When x denote the solution of t for the initial condition x(t0) = a. contractive after a small overshoot (SO) ′, with e2 < 0, so this is not a feasible solution. Substituting R2 in e = [0.4305 0.4695]′ ∈ C2, which is the unique feasible solution. Thus, the steady-state output rate is R2 = −0.3046.The polynomial equation admits several solutions for R. The next result shows that the degree of this polynomial equation grows quickly with n.In general, Proposition 4Consider the MFALK with dimensionnand with λi ≠ γi, αi ≠ 0, βi ≠ 0, for alli. Then genericallymay be written asw(R) = 0, wherewis a polynomial of degreeand with coefficients that are algebraic functions of the rates.n.We note that this exponential increase in the degree of the polynomial equation is a feature of the MFALK that does not take place in the RFM. Indeed, in the RFM the degree of the polynomial equation for the steady-state production rate grows linearly with R) depend on the parameters. Indeed, if R > 0 [R < 0] then there is a net steady-state flow from left to right [right to left]. The next subsection describes a special case where this question can be answered rigorously.Let βi = αi = 0, i = 1, …, n, i.e. a system with no internal attachments and detachments, When Proposition 5Consider the case whereαi = βi = 0, i = 1, …, n, and suppose thatholds. ThenIn particular, ifthenR = 0, and for anyi,i is the product of all the forward rates up to i, that is, λ0λ1 … λi−1 normalized by the sum of two terms: the product of all the forward rates up to i and the product of all the backward rates up to i.T. This may be interpreted as a periodic excitation feeding the MFALK. Many biological processes are affected by such excitations due for example to the periodic 24h solar day or the periodic cell-cycle division process. For example, translation elongation factors, tRNAs, translation and transcription initiation factors, ATP levels, and more may change in a periodic manner and this may be modeled using periodic rates in the MFALK.Assume now that some or all the rates are time-varying periodic functions with the same period T? We will show that this is indeed so, i.e. the MFALK entrains to a periodic excitation in the rates. In order to understand what this means, consider a different setting, namely, using the MFALK to model traffic flow. Then the rates may correspond to traffic lights, changing in a periodic manner, and the state-variables are the density of the moving particles (cars) along different sections of the road, so entrainment corresponds to what is known as the “green wave”  = f(t) for all t. Assume that the λis, γis, αis and βis are uniformly bounded, non-negative, time-varying functions satisfying:We say that a function T > 0 such that all the λi(t)s, γi(t)s, αi(t)s, and βi(t)s are T-periodic.there exists a  c1, c2 > 0 such that at least one of the following two conditions holds for all time tthere exist c3 > 0 such thatthere exists Periodic MFALK (PMFALK).We refer to this model as the Theorem 1Consider the PMFALK with dimensionn. There exists a unique functionthat isT-periodic, and for anya ∈ Cnthe trajectoryx converges toϕast → ∞.entrains (or phase-locks) to the periodic excitation in the parameters. In particular, this means that the output rate R(t) in T-periodic function:T ≥ 0, Theorem 1 implies that entrainment holds also in the particular case where a single parameter is oscillating (with period T > 0), while all other parameters are constant. Note also that Corollary 1 follows from Theorem 1.Thus, the PMFALK Example 3 Consider the MFALK with dimension n = 3, parameters: λ0(t) ≡ 1.0, λ1(t) ≡ 1.2, λ2(t) = 1 + 0.5sin(πt/4), λ3(t) ≡ 0.9, γ0(t) ≡ 0.4, γ1(t) = 0.4(1 + sin((πt/4) + 1/2)), γ2(t) ≡ 0.25, γ3(t) ≡ 0.45, α1(t) ≡ 0, α2(t) ≡ 0.05, α3(t) ≡ 0, β1(t) ≡ 0, β2(t) = 0.05(1 + sin((πt/2) + 1/4)), β3(t) ≡ 0, and initial condition x(0) = [0.8 0.8 0.8]′. Note that all the rates here are periodic, with a minimal common period T = 8. xi(t), i = 1, 2, 3, as a function of t. It may be seen that each state variable converges to a periodic function with period T = 8.Since the MFALK is a mean-field approximation of ASEP with Langmuir kinetics, a natural question is does ASEP with Langmuir kinetics entrains as well (in some stochastic manner)? The following example addresses this question using Monte Carlo simulations.Example 4 Consider ASEP with Langmuir kinetics with N = 8 sites and hopping rates:i ≠ 1,γi(t)≡0, i = 0, …, 8,αi(t)≡0, i = 1, …, 8,βi(t) ≡ 0, i ≠ 6,T = 1E7. Note that all these rates are periodic with a common minimal period T. When simulating ASEP with Langmuir kinetics, these rates are used to determine the next event time . Given a corresponding rate r(t) at time t, the next event time is t + p(r(t)), where p(r(t)) is a random variable drawn from the exponential distribution with mean r(t).where E8 time ticks. E6 consecutive occupancies (here of course the occupancies are either 0 or 1). For example, the value depicted at time segment 1 is the average occupancy in the time interval . Note that since T = 1E7 time ticks, the period T is equal to 10 segments. It may be seen that all the average occupancies indeed entrain to the periodic excitation. In particular, they are periodic (up to the noise induced by the stochastic process) with a period of 10 segments. Thus, our simulations do suggest that some form of entrainment also takes place in ASEP with Langmuir Kinetics.We ran MATLAB simulations of this process for 1a ≤ b if (b − a) ∈ K; a < b if a ≤ b and a ≠ b; and a ≪ b if (b − a) ∈ int(K). The system monotone if a ≤ b implies that y ≤ y for all t ≥ 0. In other words, the flow preserves the partial ordering  rates decreases [increases] the steady-state density in all the sites. Also, an increase in any of the internal detachment [attachment] rates decreases [increases] the steady-state output rate. The next example demonstrates this.Example 5 Consider the MFALK with n = 3, λi = 1, γi = 0, i = 0, 1, 2, 3, βi = α3 = 0, i = 1, 2, 3. R as a function of α1 ∈  and α2 ∈ . It may be seen that R decreases with both α1 and α2.We note that the analytical results in Proposition 7 agree well with the simulation results obtained using a TASEP model for translation that included alternative initiation along the mRNA and ribosome drop-off .The next section describes an application of the MFALK to a biological process.It is believed that during mRNA translation ribosome movement is unidirectional from the 5’ end to the 3’ end, and that ribosomes do not enter in the middle of the coding regions. However, ribosomes can detach from various sites along the mRNA molecule due to collisions between ribosomes, for example. This is known as ribosome drop off.As mentioned in the introduction, ribosome drop off has been the topic of numerous studies –51, 68. −4 − 10−3 abortion events per codon in E. coli. In other words, at each codon one out of 1,000–10,000 decoding ribosomes aborts. This value is non-negligible. If we consider a drop-off rate of 4 × 10−4 per codon along a coding region of 300 codons (approximately the average coding region length in E. coli) then on average, around 10 out of every 100 ribosomes will fail to complete translation of the mRNA.It is clear that ribosome abortion has drawbacks. Indeed, translation is the most energetically consuming process in the cell, and abortion results in truncated, non-functional and possibly deleterious proteins. It is believed that transcripts undergo evolutionary selection to minimize abortion and/or its energetic cost , 51–53. γi = 0 (i.e. no backwards motion) and βi = 0  for all i. Changing the values of the αis allows to model and analyze the effect of ribosome drop off at different sites along the mRNA molecule. We assume thatCn w.r.t. the L1 norm, and thus admits a unique globally asymptotically stable steady-state e ∈ int(Cn).To model translation with ribosome drop off, we use the MFALK with S. cerevisiae genes  was inferred based on the fact that the ribosome density (and the translation rate) is proportional to the initiation rate when it is rate limiting . In these figures the genes in the legends are sorted according to their expression levels: the gene at the top (YGR192C) has the highest mRNA levels while the gene at the bottom (YER106W) has the lowest levels. It may be seen that as the drop off (detachment) rate αc increases from 10−4 to 10−2, ρ decreases by about 30%, and R decreases by about 50%. This demonstrates the significant ramifications that ribosomal drop off is expected to have on translation and the importance of modeling drop off. It may also be observed that in general for mRNAs with higher expression levels (i.e. mRNAs with higher copy number in the cell) the reduction in both the steady-state production rate and mean density due to drop off is lower as compared to the reduction for mRNAs with low copy number.LetS. cerevisiae genes; 2) most expressed S. cerevisiae genes (top 20%); and 3) least expressed S. cerevisiae genes (bottom 20%). The average reduction in R and ρ over these three sets of genes are depicted in Figs We next evaluated the reduction due to drop off over: 1) all 6310 protein-encoding In many important processes biological “particles” move along some kind of a one-dimensional “track”. Examples include gene transcription and translation, cellular transport, and more. The flow can be either bidirectional (as in the case of transcription) or unidirectional (as in the case of translation elongation), with the possibility of both attachment and detachment of particles at different sites along the track. For example, motor proteins like kinesin and dynein that move along a certain microtubule may detach and attach to an overlapping microtubule.T > 0) in any of its rates, i.e. the densities along the chain, as well as the output rate, converge to a unique period solutions with period T.To rigorously model and analyze such processes, we introduced a new deterministic mathematical model that can be derived as a dynamic mean-field approximation of ASEP with Langmuir kinetics, called the MFALK. Our main results show that the MFALK is a monotone and contractive dynamical system. This implies that it admits a globally asymptotically unique steady-state, and that it entrains to periodic excitations  → R is strictly concave, and that sensitivity analysis of R is an eigenvalue sensitivity problem [R = R in the MFALK can also be described using such a linear-algebraic approach.Topics for further research include the following. In the RFM, it has been shown that the steady-state production rate is related to the maximal eigenvalue of a certain non-negative, symmetric tridiagonal matrix with elements that are functions of the RFM rates, i.e. the λis . This im problem . Similar problem  which isThe application of the MFALK to model ribosome drop off suggests an interesting direction for further study, namely, how to design genes that minimize the drop off rate.Another research direction is motivated by the fact that many of the transport phenomena that can be modeled using the MFALK do not take place in isolation. For example, many mRNA molecules are translated in parallel in the cell. Thus, a natural next step is to study networks of interconnected MFALKs. In this context, ribosome drop off may perhaps increase the total production rate in the entire system, as it allows ribosomes to detach from slow sites, enter the pool of free ribosomes, and then attach to the initiation sites of other, less crowded, mRNA molecules. However, drop off still incurs the biological “cost” associated with the synthesis of a chain of amino-acids that is only a part of the desired protein. The fact that the MFALK is contractive may prove useful in analyzing networks of MFALKs, as there exist interesting results proving the overall contractivity of a network based on contractivity of the subsystems and their couplings .Finally, another interesting topic for further research is studying the effect of controlled detachment rates on the formation of traffic jams. Indeed, it is known that kinesin-family motor proteins are more susceptible to dissociation when their path is blocked , 15.S1 FileThis file includes the proofs of all the theorems in the paper.(PDF)Click here for additional data file.


Here, we report 11 single-particle cryo-electron microscopy (cryoEM) reconstructions of the complex of bacterial 30S subunit with initiator tRNA, mRNA, and IFs 1–3, representing different steps along the initiation pathway. IF1 provides key anchoring points for IF2 and IF3, thereby enhancing their activities. IF2 positions a domain in an extended conformation appropriate for capturing the formylmethionyl moiety charged on tRNA. IF3 and tRNA undergo large conformational changes to facilitate the accommodation of the formylmethionyl-tRNA (fMet-tRNA  •Structures of the 30S ribosomal subunit with initiation factors, tRNA and mRNA•IF3 helps to position the correct start codon in the P site before binding of tRNA•Large-scale conformational changes of IF3 and tRNA are observed•IF3 movements facilitate the accommodation of initiator tRNA in P site Bacterial ribosomes can start scanning for start codons only after undergoing large scale conformational changes governed by three key initiation factors. IF3 is a two-domain protein that plays a particularly important role in the fidelity of selection of tRNAfMet and correct start codon. Although eukaryotic initiation is far more complex, each of these three IFs has a homolog in eukaryotes and in archaea.Translational initiation is highly regulated and involves initiation factor (IF)-mediated positioning of an initiator tRNA over an mRNA start codon at the P site of the small ribosomal subunit. In bacteria, translational initiation is controlled by just three IFs, which bind cooperatively to the small ribosomal subunit (30S). IF1 is a small protein that binds in the 30S A site. IF2 is a large GTPase that helps recruit formylmethionyl-tRNA  sequence with the anti-Shine Dalgarno (ASD) sequence at the 3′ end of 16S rRNA. Interaction of a tRNA anticodon with a candidate start codon in the presence of all three IFs defines a 30S pre-initiation complex (PIC). In a poorly understood series of steps, the IFs either directly or indirectly examine the tRNA initiator identity elements and the codon:anticodon interaction. A cognate codon:anticodon interaction is thought to induce a conformational change from the 30S PIC to a 30S initiation complex (30S IC) more favorable for the binding of the 50S subunit to form a 70S initiation complex (70S IC). At some point IF3 dissociates, GTP is hydrolyzed on IF2, followed by dissociation of IF2 and IF1. Initiation ends with an elongation-competent 70S ribosome containing a P-site fMet-tRNAfMet .IF3 discriminates against elongator tRNAs and monitors the codon:anticodon interaction . IF3 desThese complex initiation events in the 30S PIC are not well understood structurally. IF1 is known to enhance the activities of both IF2 and IF3, but the structural basis for this enhancement is not clear from either a high-resolution structure of 30S•IF1  or from Thermus thermophilus 30S PIC at resolutions as high as 3.6 Å, representing distinct steps during initiation. IF1 provides key anchoring points for IF2 and IF3, thereby enhancing their activities. IF2 positions its C2 domain in a conformation suitable for binding the formylmethionyl moiety attached to the tRNAfMet. We observe large-scale changes in IF3 location and conformation that define distinct roles of IF3 along the initiation pathway. The structures suggest mechanisms for many of the discrimination functions of IF3, including roles in preselecting a good candidate start codon even before tRNA binds, and also in delaying a committed step in ribosomal subunit association.Here, we present 11 structures of fMet and represent states after binding of tRNA but prior to its full accommodation in the P site. PIC-2A shows the initial binding of tRNA at P site in an open conformation of 30S. PIC-2C shows tRNA in a closed conformation of 30S, while PIC-2B represents a likely intermediate step between PIC-2A and 2C. PIC-3 shows a more engaged fMet-tRNAfMet, while PIC-4 shows a fully accommodated fMet-tRNAfMet at the P site.We have determined single-particle cryoEM structures of the 30S PIC from two samples, respectively with and without IF2 . Maps defMet and represents the 30S PIC after binding of tRNA but prior its accommodation in the P site. Here too the positions of IF1 and IF3 are similar to those observed in PICs 2A–2C lacking IF2. PIC-III with IF1, IF2, IF3, mRNA, and fMet-tRNAfMet represents the 30S PIC after accommodation of tRNA in the P site and is very similar to the equivalent IF2-free PIC-4.Structures from sample 2 (with IF2) are named PIC-I to III. PIC-I with IF1, IF2, IF3, and mRNA represents a state prior to the binding of tRNA. The positions of IF1 and IF3 are very similar to those observed in the equivalent PICs 1A–1C lacking IF2. This is consistent with the observation of similar intramolecular IF3 smFRET for complexes with and without IF2 . PIC-II fMet. In each structure, the NTD and CTD occupy different positions near the 30S platform and P site, respectively. Thus, IF3 is observed in distinct conformations in the various 30S PICs, which we propose represent different stages of initiation.As observed with other ribosomal complexes, the local resolution and the quality of the density are best in the core of the 30S and in ligands directly attached to it . In all A comparison of all 30S PICs presented here shows various positions of the head of 30S with respect to the body, with motions best described as swiveling and/or nodding of the head. An overall rotation of 8–9° of the 30S head along 16S rRNA helix 28 (h28), which forms the “neck” connecting head to the body, is observed among the complexes . The 30SApart from the swiveling of the head, a previously unobserved upward movement of the 30S head is also observed in two structures corresponding to early steps in the pathway (PICs 1C and 2A). This movement opens the A-site mRNA latch formed between h18 and h34  Figure  similar Escherichia coli numbering of 16S rRNA). The CTD density is observed at the top of h44 adjacent to the P site making interactions with both h44 (1494–1497), h24, and with G1517 of h45. The residues of IF3 involved in binding 16S rRNA  are mainly hydrophilic and basic. This position of IF3 on 30S agrees with the 16S rRNA cleavage in directed hydroxyl radical probing experiment of 30S⋅IF3 complex (E. coli numbering), a NTD/linker residue that when mutated reduces the fidelity of initiation (In tRNA-free structures (PICs 1A–1C), the dumbbell-shaped density is seen for nearly all of IF3 . The NTD complex . It is a complex  and othe complex . Tyr75 , the IF3 NTD moves 9 Å away from the platform to interact with the elbow of tRNA, while the CTD remains in its position at the P site  even though the 30S is in a closed conformation. However, when the fMet-tRNAsition 1 C. In sucfMet (In the first tRNA-accommodated CTD location (PIC-3), the CTD rotates around a helix (residues 97–112) to position the β-hairpin away from the P site and fMet-tRNAfMet . This pofMet B and abofMet D. This rfMet D. In thifMet but is 13 Å away compared to PIC-3. In position 2, the CTD is moved away and hence loses contact with ASL (In the structure with a second tRNA-accommodated CTD location (PIC-4), the CTD is observed far away from P site with minimal overlap with either position 1 or 1′. The β-hairpin is now positioned 25 Å away from that of position 1′ C. The CTwith ASL C. The CTwith ASL  is similfMet in a way that would be compatible with 50S joining, so this may be the last interaction of IF3 in the 70S initiation complex . In the structures reported here, the SD:ASD helix lies closer to uS11 and uS7 A, and faIn tRNA-free PICs 1A–1C, we observe an unusual mRNA conformation, in which a base at −1 position of mRNA stacks with G926 in the E site, while the next base, the A+1 of the AUG codon, stacks with A790 in the P site A. The baThough the density of the mRNA purine stacked on A790 is most likely A+1, at the present resolution we cannot rule out the possibility that it is G+3 instead. Indeed, this A790 site appears more favorable for binding of G rather than A. It is possible that G+3 binding to this site precedes the A+1 binding we see here, and that IF3 moves the start codon by two nucleotides toward the P site, consistent with the known ability of IF3 to move the mRNA start codon toward the P site . RegardlfMet, as it has an open conformation of 30S with a widened P site, and G1338 and A1339 of 16S rRNA are correctly positioned for interactions with the three highly conserved G:C base pairs in the ASL , the CTD would clash with tRNA in later, accommodated states; hence, it is clear that the CTD has to move to allow tRNA to be completely engaged in P site. IF1 provides a few highly conserved anchoring points for CTD in this position, in which the tRNA has not yet been “approved.” Thus, IF1 appears to stabilize a more discriminatory state of the CTD, consistent with observations that IF1 and IF3 bind cooperatively.fMet.The interactions between residues in the CTD and the codon in the P site observed before tRNA binding suggest a possible sequence-dependent role for IF3 in directly identifying and stabilizing a good candidate start codon, consistent with the known ability of IF3 to move mRNA  as well fMet binding is likely to be PIC-2A. However, at this resolution we cannot rule out the possibility that the purine stacked on A790 is G+3 rather than A+1. If we interpret this purine as G+3 rather than as A+1, then the start codon would be largely in the E site. In this context, we note that the tRNA in PIC-2B is positioned more in the E site than in the P site, raising the possibility of an alternative sequence of events to the one proposed here. It is also possible that the initial steps in the pathway are not linear, but we propose that all pathways converge onto PIC-2C followed by PIC-3 and PIC-4  and CTD (interacting with the D-loop and ASL) come closest to each other in this conformation. In PIC-4, the CTD is moved further away from ASL to position 2, and the two domains of IF3 are furthest apart in this conformation. Although the CTD in positions 1 and 1′ is very close to the ASL, we do not observe a direct monitoring of conserved G:C base pairs of the ASL by IF3. The presence of conserved G:C base pairs in the ASL and its recognition by A-minor interactions by 16S may provide tRNAfMet the additional binding energy to allow it to overcome the steric block as well as electrostatic repulsion of the CTD, thus remaining stably bound where other tRNAs are likely to dissociate.In PIC-3, the two domains of IF3 closely wrap around the tRNAThe linker between the two domains of IF3 adopts a helical structure throughout the initiation pathway. This may be important to coordinate the two separate and distinct movements of the N- and C- domains of IF3. The NTD remains attached to the tRNA elbow, while it rotates from the E to P site and tilts toward the body. The CTD, on the other hand, moves from top of the h44 at the P site to a position away from it and lower on h44. The observed movement of CTD along the initiation pathway may be due to a combination of a steric displacement by the correct ASL and codon at the P site and a pulling action of the NTD as it moves with the elbow of tRNA. With an incorrect tRNA, the CTD would not be displaced by an ASL accommodating into the P site, while the NTD may not bind as tightly to the elbow of a non-initiator tRNA.fMet. However, it has also been suggested that IF2 and initiator tRNA arrive together on the 30S  as seen in PIC-I. The placement of the C2 domain thus facilitates its binding to the extended CCA of tRNA as seen in PICs II and III. This binding could provide additional specificity for fMet-tRNA the 30S , in whic the 30S  shows thA comparison of the structures with and without IF2 also shed some light on the dissociation of IF1. In PIC-III, we observe IF1 present in the PIC, while the CTD has moved to position 2. In PIC-4, the equivalent complex without IF2, no density is observed for IF1. Thus, one role of IF2 may be to keep IF1 in place in the later steps of initiation. The absence of IF1 in PIC-4 may be due to h44—an important part of the IF1 binding site—adopting a conformation unfavorable for IF1 once the CTD moves to position 2. This is consistent with previous studies showing changes in h44 in the presence of cognate tRNA and correct start codon .The present study sheds light on the multiple steps used to position the correct tRNA and codon during bacterial initiation. It provides insights into the role of each IF, especially of IF3, seen in several previously unobserved states. There are some inherent similarities between this bacterial initiation pathway and eukaryotic initiation. The open and closed conformations of the small subunit and the swiveling of the head play a key role in both. IF1 plays a role similar to eIF1A in eukaryotes in blocking the A site and also providing anchoring points to other factors. This study shows an interesting similarity between IF3-CTD and eIF1, which occupy similar sites in the small subunit and play a crucial role in the fidelity of the process. Both factors have to be displaced from their initial position to allow complete accommodation of tRNA. The IF3-NTD may play a role similar to eIF2α (domain D2) in capturing the elbow of the initiator tRNA. In both cases, the initiator tRNA probably binds in a widened P site and then is subsequently accommodated in a narrowed P site via a series of conformational changes. The binding of the CCA of initiator tRNA is similar in C2 of IF2 and domain IV of the homologous eIF5B. Thus, despite the large differences in the complexity and regulation of initiation in bacteria and eukaryotes, both seem to employ a common core mechanism with the three factors that are common to both kingdoms.ramak@mrc-lmb.cam.ac.uk).Further information ad requests for reagents may be directed to, and will be fulfilled by the corresponding author V. Ramakrishnan (Thermus thermophilus (strain HB8) was grown in ATCC 697 medium consisting of yeast extract, polypeptone peptone, sodium chloride and agar; and adjusted to pH 7.5. The ATCC 697 medium was supplemented with Castenholz salts and the cells were grown under vigorous aeration at 72-75°C in the University of Georgia fermentation facility.Thermus thermophilus 30S ribosomes . The cells were passed through the cell disrupter (Emulsiflex) once at 25,000 Psi and the cell lysate was run (centrifugation) in a 45Ti rotor for 30 min at 30,000 rpm. After the spin, the supernatant was carefully collected without disturbing the pellet consisting of cell debris and layered over a sucrose cushion 2, 0.5 mM EDTA and 20 mM HEPES, pH 7.5 in 45Ti tubes. The ribosomes were pelleted overnight at 43,000 rpm for 18.5 hr. The supernatant was removed and the pellet obtained contained ribosomes. The pellet was washed with buffer (1.5 M (NH4)2SO4, 10 mM Mg(OAc)2, 400 mM KCl and 20 mM Tris-Cl, pH 7.5) and then resuspended and loaded onto Toyopearl butyl-650S column equilibrated with the same buffer. The 70S was eluted by an inverse gradient of (NH4)2SO4. The fractions containing 70S were collected, pooled and pelleted overnight at 43,000 rpm for 18.5 hr in a sucrose cushion. The 70S pellet was resuspended and loaded on 15%–30% sucrose gradient. This gradient was centrifuged in SW28 rotor at 20,000 rpm for 20.5 hr. After the run, the gradient was fractionated and the 70S containing fractions were collected. Care was taken to remove any 50S containing fractions. The 70S containing fractions were again pelleted overnight at 43,000 rpm for 18.5 hr in a sucrose cushion. The purified 70S was then resuspended in a buffer with only 2.25 mM Mg(OAc)2 and loaded on 10%–30% sucrose gradient. This gradient was centrifuged in SW28 rotor at 28,500 rpm for 19 hr to separate out 30S and 50S peaks. After the run, the gradient was fractionated and the 30S containing fractions were collected. Care was taken to remove any 50S containing fractions. The purified 30S was buffer exchanged to a storage buffer 2 and stored as small aliquots in −80°C after flash freezing in liquid nitrogen.The complete purification of ibosomes  was doneAUGUUCA-3′ (the codon for fMet is underlined). This sequence is modified from that of Z4C in (The mRNA oligonucleotide was purchased from Integrated DNA Technologies. The sequence of mRNA was 5′GCUCUUUUAACAAUUUAUCAGGCAAGGAGGUAAAAf Z4C in .2) and routine phenol extraction was performed where the tRNA was ethanol precipitated in the final step. The precipitate was dissolved in Q-sepharose buffer  and loaded on Q-sepharose column ,B)Class B showing density for mRNA, tRNA, IF1 and IF3 (in Position1) , andC)Class C containing mRNA, tRNA, IF1 and IF3 (in Position2 in low occupancy) .The preliminary 3D rounds of classification showed 30S in different conformations and tentative positions of IFs and tRNA. Next, we decided to apply a strategy based on the recently reported method of masked classifications with subtraction of the residual signal , by crea1)30S in a closed conformation 2)30S in a closed conformation but with head swiveled  and3)30S in an open conformation .Class ‘A’  was further classified by standard 3D classification in three classes:1)30S in an open conformation ,2)30S in a closed conformation but with head swiveled  and3)30S in a closed conformation .Class B  was also further classified in 3 classes as:1)30S PIC with mRNA, tRNA, IF1 and IF3 in Position1’ ,2)30S PIC with mRNA, tRNA, and IF3 in Position2  and3)30S PIC with mRNA and tRNA Class C  was further classified into 3 classes using ‘ligand mask’:1HR0) as in Sample1 low-pass filtered to 40 Å. Next a masked 3D classification into 10 classes was carried out. The mask around the region on the ribosome where IF2 binds (‘IF2 mask’) based on the low-resolution cryoEM structure of 30S-IF2 30S with IF1, IF3 (in Position1) and mRNA without tRNA ,B)30S with IF1, IF3 (in Position1), mRNA and tRNA , andC)30S with IF1, IF3 (in Position2), mRNA and tRNA .We followed a similar strategy of ‘focused’ 3D classification with ‘ligand mask’ to isolate three well-defined types of complexes:Both movie processing  in RELIOMaps of PICs obtained from Sample1  are named from PIC-1 to 4. Maps of PICs obtained from Sample2 (with IF2) are named as PIC-I to III.)The various PIC structures are named in an order that represents one possible initiation pathway in which mRNA binding precedes tRNA binding. The primary criterion for ordering the structures was to minimize compositional and conformational differences between successive states (see below for details). In such an “mRNA-first” pathway, at least one of the tRNA-free states must occur first, and the final state must be the one with a tRNA fully accommodated in the 30S P site, where “fully accommodated” is defined by comparing with other reported structures.1)30S conformation: open/closed conformations and swivel of the head2)Presence or absence of tRNA & its accommodation in the P site3)IF3-CTD position and conformational changes like position of its β- hairpin relative to ASL4)Must be more closely related to the previous state or subsequent state than the other complexes5)Must result in a consistent and reasonable pathway for initiation with minimal conformational excursionsThings taken into consideration while naming the complexes:PIC-1A contains 30S, IF1, IF3 and mRNA. IF1 is at the A site. IF3- NTD is at the platform while the CTD is at the P site (Position1). The 30S is in the closed conformation with compressed h28. The 30S head is observed in canonical position as in 70S ribosomes . The P sPIC-1B contains 30S, IF1, IF3 and mRNA. IF1 is at the A site. IF3- NTD is at the platform while the CTD is at the P site (Position1). The 30S is in the closed conformation but h28 is relaxed and the 30S head is swiveled. The P site would be compatible for the loading of tRNA with minor rearrangements. However, the mRNA latch is closed and the P site is narrow.PIC-1C contains 30S, IF1, IF3 and mRNA. IF1 is at the A site. IF3- NTD is at the platform while the CTD is at the P site (Position1). The 30S is in the open conformation with relaxed h28 and the head is not swiveled. The P site is compatible for the loading of tRNA due to the presence of a widened P site with no obstruction from CTD at Position1.fMet. IF1 is at the A site. IF3- NTD is away from platform and now in contact with elbow of fMet-tRNAfMet while the CTD is at the P site (Position1). The 30S is in the open conformation with a relaxed h28 and the head is not swiveled. The P site is widened. The ASL is tilted away from the 30S body. The CTD is in Position1 with a subtle movement of β- hairpin of IF3 away from ASL.PIC-2A contains 30S, IF1, IF3, mRNA and fMet-tRNAfMet. IF1 is at the A site. IF3- NTD is away from platform and in contact with elbow of fMet-tRNAfMet while the CTD is at the P site (Position1). The 30S is in the closed conformation but h28 is still relaxed and the 30S head is swiveled. The P site is relatively narrowed. The ASL is moved toward the E site. The CTD is in Position1. We have named it as PIC-2B after PIC-2A because a head swivel in PIC-2A would enable the PIC-2B conformation.PIC-2B contains 30S, IF1, IF3, mRNA and fMet-tRNAfMet. IF1 is at the A site. IF3- NTD is away from platform and now is contact with elbow of fMet-tRNAfMet while the CTD is at the P site (Position1). The 30S head is observed in the canonical conformation as in 70S ribosomes . The 30S head is observed in canonical position as in 70S ribosomes . The 30S head is observed in canonical position as in 70S ribosomes . The 30S head is observed in canonical position as in 70S ribosomes .fMet. IF1 is at the A site. IF3- NTD is away from platform and in contact with elbow of fMet-tRNAfMet while the CTD is at the P site (Position1).PIC-II contains 30S, IF1, IF2, IF3, mRNA and fMet-tRNAfMet. IF1 is at the A site. IF3- NTD is in contact with elbow of fMet-tRNAfMet while the CTD is relocated away from P site (Position2). The 30S head is observed in canonical position as in 70S ribosomes  (1HR0) (Geobacillus stearothermophilus IF3 (PDB: 1TIF and PDB: 1TIG) (T. thermophilus IF2 (PDBs: 3J4J and PDB: 4KJZ) (4WZO)   were plaB: 1HR0) . Next, t) (1HR0) , the N aB: 1TIG) , T. therB: 4KJZ)  (Eiler eB: 4KJZ)  and tRNA) (4WZO)  were doc) (4WZO)  and furt1TIF and PDB: 1TIG) helped in the model building almost complete IF3 (residue 3 to 170) with the helical linker joining the two domains. Special attention was devoted toward modeling of domain C2 of IF2. Rigid body fitting the NMR structure of C2 of IF2 from Bacillus stearothermophilus (PDB: 1D1N) (1G7T) (1ZO1)   was carrB: 1D1N) . It is aB: 1D1N)  and with) (1G7T) . In PIC-) (1ZO1) . Refinem) (1ZO1) , using e) (1ZO1) . Average) (1ZO1) . Cross-v) (1ZO1) . Refinem) (1ZO1)  or Chime2801% of 4.10 and 5.77, respectively. The molar absorption value was estimated from the amino acid sequences, using the ProtParam tool of the Expasy database. The 30S ribosomal subunit was measured by its optical absorption at 260 nm.IF1 concentration was measured by the method of Bradford using commercial reagent from Bio-Rad and bovine serum albumin as a standard. For accurate estimation of the molar concentration of IF2 and IF3, these proteins were quantitated from its optical absorption at 280 nm, using an AResolutions reported here are based on the gold-standard FSC = 0.143 criterion . All mapThe 30S head rotation was measured by using h28 (at the neck) as rotation axis and C1030b, at the 30S beak, as the reference point.5LMN, PDB: 5LMO, PDB: 5LMP, PDB: 5LMQ, PDB: 5LMR, PDB: 5LMS, PDB: 5LMT, PDB: 5LMU and PDB: 5LMV for PIC-1A, PIC-1B, PIC-1C, PIC-2A, PIC-2B, PIC-2C, PIC-3, PIC-4 and PIC-III, respectively.Eleven maps have been deposited in the EMDB with accession codes EMDB: 4073, EMDB: 4074, EMDB: 4075, EMDB: 4076, EMDB: 4077, EMDB: 4078, EMDB: 4079, EMDB: 4080, EMDB: 4081, EMDB: 4082 and EMDB: 4083 for PIC-1A, PIC-1B, PIC-1C, PIC-2A, PIC-2B, PIC-2C, PIC-3, PIC-4, PIC-I, PIC-II and PIC-III, respectively. Atomic coordinates have been deposited in the PDB with accession codes PDB: T.H., J.L.L., and B.T.W. made the samples, collected and analyzed the data, determined the structures, and wrote a first draft of the manuscript. V.R. supervised the work, and V.R. and J.S.K. helped to write the manuscript.


Terrestrial freshwater runoff strongly influences physical and biogeochemical processes at the fjord scale and can have global impacts when considered at the Greenland scale. We investigate the performance of the HIRHAM5 regional climate model over the catchments delivering freshwater to Tyrolerfjord and Young Sound by comparing to the unique Greenland Ecological Monitoring database of in situ observations from this region. Based on these findings, we estimate and discuss the fraction of runoff originating from glacierized and non-glacierized land delivered at the daily scale between 1996 and 2008. We find that glaciers contributed on average 50–80% of annual terrestrial runoff when considering different sections of Tyrolerfjord–Young Sound, but snowpack depletion on land and consequently runoff happens about one month earlier in the model than observed in the field. The temporal shift in the model is a likely explanation why summer surface salinity in the inner fjord did not correlate to modelled runoff.The online version of this article (doi:10.1007/s13280-016-0876-4) contains supplementary material, which is available to authorized users. By downscaling the HIRHAM5 grids to a finer spatial resolution, we accurately represent the complex glacier margins and estimate the fraction of freshwater discharge to the sea accounted for by glacier runoff.http://data.g-e-m.dk/.Seawater observations are available covering the entire length of Tyrolerfjord and Young Sound, but by far most terrestrial monitoring data relevant to this work are concentrated within the Zackenberg River catchment, primarily in the surroundings of Zackenberg Research Station  and on A.P. Olsen Ice Cap. A campaign to measure freshwater discharge from three different rivers was carried out in 2012  at several locations in the Zackenberg River catchment. We use 1996–2012 air temperature at 2 m and precipitation data from the longest meteorological time series, produced by the GEM ClimateBasis Programme at the climate masts close to the Zackenberg Research Station, and 2008–2012 air temperature at 2 m from the lowest AWS operated by the GEM GlacioBasis Programme on the outlet glacier of A.P. Olsen Ice Cap Fig. . All metGlacier surface mass balance in the ablation area of the monitored outlet glacier of A.P. Olsen Ice Cap is measured using ablation stakes drilled into the ice and measured down to the ice surface every year in late April. They are re-measured the following year to quantify the amount of ice lost in the intervening ablation season. The surface mass balance observations used in the present study refer to 9 stakes located from 550 to 880 m above sea level (a.s.l.) and all years below the glacier equilibrium line; therefore, no accumulation observations were used. Details of the glacier mass balance programme at A.P. Olsen Ice Cap are given by Citterio et al. .2, respectively  stations in the fjord and on the coastal shelf Fig.  by the GThe high-resolution HIRHAM5 RCM is based on the physical parameterizations of the ECHAM5 global climate model , land/glacier mask and land/ocean mask Fig. . Higher S2).The HIRHAM5 daily grids of 2-m air temperature, precipitation, snow melt, ice melt, snow water equivalent (SWE), glacier mass balance, glacier runoff and land runoff were nearest neighbour resampled from 0.05° by 0.05° (ca. 5.5 by 5.5 km) to ca. 110 by 110 m. The cell size was chosen to be a factor of 5–10 smaller than the width of the A.P. Olsen Ice Cap outlet glacier tongue where surface mass balance observations are collected. Since Freya Glacier is narrower, its mass balance observations were not used. The HIRHAM5 glacier/land and land/ocean masks were instead replaced with the 110 by 110 m surface type masks gridded from the PROMICE and GEUS vector maps. Missing or excess glacier cover in the 0.05° by 0.05° HIRHAM5 glacier mask was corrected by zeroing the glacier runoff at non-glacier cells or by filling in a glacier runoff estimate from HIRHAM5 glacier cells at similar elevation in the surrounding region  was calculated as the elevation difference times the vertical temperature lapse rate estimated daily by least squares fitting of HIRHAM5 elevations and 2 m air temperatures in the surrounding region. These daily temperature lapse rates were estimated separately over melting versus non-melting cells, because a fixed 0 °C surface has a buffering effect on 2 m air temperatures and resulting in different lapse rates. This downscaling approach corrects elevation bias at the sub-RCM cell scale but it does not introduce any regional bias  and it preserves the HIRHAM5 spatial variability at the scale of the RCM grid cells .Each day, melting versus non-melting HIRHAM5 grid cells were identified based on positive values of either ice or snow melt. Elevation bias results from the vertical separation between the elevation of each 110 by 110 m grid cell and that of the corresponding 0.05° by 0.05° HIRHAM5 cell. For instance, the A.P. Olsen AWS used to compare 2 m air temperature is located at ca. 660 m a.s.l. but HIRHAM5 models the corresponding 0.05° by 0.05° grid cell as laying at 1030 m a.s.l. because of the high topography surrounding the glacier tongue. The elevation bias correction for 2 m air temperature . At the climate mast close to Zackenberg Research Station, the median downscaled model bias is −1.54 °C (1996–2012), with a larger variability of daily average 2 m temperature around the median of the period . At both sites, the variability of daily average 2 m temperature and model bias are larger during the cold season, when similar or lower temperatures than at the glacier AWS (660 m a.s.l.) are commonly observed at the lower lying climate mast close to sea level.Downscaled HIRHAM5 modelled 2-m air temperature reproduces observations well, particularly over glacier surfaces where the median daily bias over the 2008–2012 period is −0.56 °C  shows that the modelled seasonal snow cover always starts depleting and disappears more than a month earlier than observed. This happens consistently in all years with available observations (1998–2012). This early modelled start of the melt season is also visible in the comparison of 1996–2012 daily model runoff versus observed Zackenberg River discharge . Here salinity is lower than the water mass below and temperatures are higher—often above 10 °C at the surface (data not shown). Salinity in the inner part of Tyrolerfjord (section 1) shows a strong influence of glacial melt water in the Tyroler Elv river and other smaller rivers contributing with glacial melt water from the Greenland ice sheet and local ice caps , which gradually increases towards the sea (section 4). In sections 3 and 4, a larger fraction of freshwater originates from land runoff north of Young Sound combined with melting sea ice. Correlation analysis between the time series in salinity from each of the four sections showed that only sections 1 and 2 were significantly correlated  and thus displayed similar interannual variation. This indicates that in sections 1 and 2, surface salinity is showing similar year-to-year variation and thus likely influenced by the same overall processes. In contrast, sections 3 and 4 are increasingly influenced by the ocean and contribution of freshwater from melting sea ice and runoff north of our study area. We thus combined sections 1 and 2 and calculated the average salinity each year for this inner part of the fjord corresponding to the intermediate catchment area, where we would expect runoff to exert the strongest control on surface salinity. Based on the overlapping time series in summer salinity and modelled runoff from 2004 to 2012, we would expect a negative relationship between runoff (we used accumulated runoff until the date of the fjord sampling took place) and summer salinity. However, the linear regression shows a positive relationship although it is not statistically significant . Significant linear regressions between runoff and fjord salinity were not found for any of the other sections. For the combined sections 1 and 2, we also calculated average salinity for the 0–5 m surface layer depth to test if the lack of relationship to runoff was sensitive to the thickness of the surface layer. However, the time series based on the 0–5 m showed similar overall patterns as the time series based on the 0–10 m surface layer and the two were closely correlated . Using the 0–5 m data series did not provide a significant relationship to the modelled runoff and neither did using the estimated runoff for the 21 days prior to the fjord sampling.The interannual variation in average salinity of the surface layer 0–10 m) for each of the four fjord sections is given in Table 0 m for eaps Fig. . This seThe modelled fraction of terrestrial runoff originating from glaciers grows during the ablation season in all modelled catchments Fig. , with glS7) to fjord sections 1 and 2, which receive most of their annual freshwater input from glaciers  difference in observed versus modelled disappearance of the snow cover over areas smaller than a single HIRHAM5 cell. The HIRHAM5 products used in this study include a subsurface scheme for meltwater retention which reduces and delays the runoff from glaciers. However, this scheme is not implemented over land, which certainly results in the modelled snowpack outside glaciers disappearing too rapidly. The modelled runoff from the Zackenberg River catchment being higher than observed river discharge in the early season and lower in the late season Fig.  is consiers Fig. , compareers Fig. .A different potential explanation could be that our measurement of salinity is somehow biased and does not capture the actual interannual variation in freshwater content. However, our time series of surface salinity is not very sensitive to changing to depth used to calculate average salinity (upper 5 or 10 m) and the two inner sections of the fjord also displayed very similar interannual variation, suggesting that this time series in fact is a robust estimate of the freshwater in the surface layer in this part of the fjord.If the model is biased towards too early depletion of the snow cover outside glaciers, the modelled fraction of terrestrial runoff originating from glaciers may evolve during the season following a different curve than modelled but reaching the same end of year values Fig. . It is iRunoff is a central driver for marine biogeochemical processes in Greenland fjords. At this point, much of our understanding of terrestrial runoff and how melt water impacts marine ecosystems in Greenland fjords originates from the two fjord systems where the GEM monitoring programmes have provided logistics, funding and data supplemented by focused research programmes. Established RCMs known to perform well over large ice masses like the Greenland ice sheet can be less accurate over the comparatively narrow transition zone between the ocean and the ice sheet, especially in regions characterized by steep and complex topography like the one discharging freshwater into Tyrolerfjord and Young Sound. The unique availability of extensive terrestrial and marine in situ observations made it possible to investigate the performance of HIRHAM5 over this region through a simple downscaling scheme. This provided an estimate of the glacial runoff signal in the terrestrial freshwater input to the sea, and its seasonal evolution, that are robust against the identified weaknesses of the available information.The HIRHAM5 model currently represents state of the art in terms of high-resolution RCM in Greenland, as it is one of the very few models run at a spatial resolution of 5.5 km. However, the complex topography in the Zackenberg region likely requires even higher spatial resolution and sophisticated non-hydrostatic model dynamics in order to accurately model complex land–ice–ocean interactions on short timescales  Ongoing development of HIRHAM5 is expected to improve the modelling of runoff from glaciers and from land, and allow detecting the terrestrial freshwater signal in the surface fjord salinity. Providing a first estimate of the runoff in other regions of Greenland will be the next step in trying to test and upscale to larger regions of Greenland the understanding gathered at the GEM sites in Zackenberg/Young Sound and in Godthaabs fjord.Supplementary material 1 (PDF 3395 kb)Below is the link to the electronic supplementary material.


The field of Arctic sea ice prediction on “weather time scales” is still in its infancy with little existing understanding of the limits of predictability. This is especially true for sea ice deformation along so-called Linear Kinematic Features (LKFs) including leads that are relevant for marine operations. Here the potential predictability of the sea ice pack in the wintertime Arctic up to ten days ahead is determined, exploiting the fact that sea ice-ocean models start to show skill at representing sea ice deformation at high spatial resolutions. Results are based on ensemble simulations with a high-resolution sea ice-ocean model driven by atmospheric ensemble forecasts. The predictability of LKFs as measured by different metrics drops quickly, with predictability being almost completely lost after 4–8 days. In contrast, quantities such as sea ice concentration or the location of the ice edge retain high levels of predictability throughout the full 10-day forecast period. It is argued that the rapid error growth for LKFs is mainly due to the chaotic behaviour of the atmosphere associated with the low predictability of near surface wind divergence and vorticity; initial condition uncertainty for ice thickness is found to be of minor importance as long as LKFs are initialized at the right locations. As for traditional weather prediction2, skilful sea ice forecasts have the potential of saving lives, support emergency management, prevent economic losses from hazardous environmental conditions; and they may also create substantial financial revenue. Such forecasts are also needed to optimize shipping routes, which could be relevant for minimizing pollution.The rapid decline of Arctic sea ice has sparked considerable interest in predicting sea ice due to the projected increase in Arctic maritime operations8. Outcomes from the Sea Ice Outlook, launched in 2008 as an open forum for predicting September Arctic sea ice extent9, are a prominent example of the progress made. For marine operations, sea ice forecasts on shorter, daily (“weather”) time scales are also critical, especially when it comes to preparation and decision making on the risk of exceptional situations and for operational decision making. Very little is known, however, when it comes to short-term predictability of Arctic sea ice11, one reason being that previous predictability research was focussing mostly on the atmosphere and non-polar regions1.Recent years have seen substantial improvements in our understanding of Arctic sea ice predictability on seasonal time scales4. However, sea ice deformation is of critical importance for decision-making in marine operations within the ice pack because deformation is related to sea ice pressure and leads. The initialization and short-term forecasting of deformation features has thus been identified as a promising as well as challenging research task12. Until recently, it was believed that classical sea ice models, employing viscous-plastic rheologies, would not be able to represent deformation-related aspects such as leads which localize along so-called Linear Kinematic Features (LKFs). Here, we determine the limits of predictability of Arctic sea ice on “weather time scales” (i.e. from 1–10 days), exploiting the fact that sea ice-ocean models at very high horizontal resolutions are actually capable of representing observed aspects related to sea ice deformation15. Furthermore, we compare outcomes for sea ice parameters traditionally considered, such as sea ice concentration, with the predictability of LKFs. Given the challenges that come with verifying LKFs due to their sharp and elongated structures, we also consider various verification methods that differ in the degree to which small errors in predicting LKF location are penalized.When it comes to sea ice, past predictability studies have considered mostly the pan-Arctic sea ice extent, or the more user-relevant location of the ice edgeThis paper is organized as follows: The methods, including the experimental setup, the detection of LKF and the determination of potential predictability are described in Section 2. This is followed by a presentation of the results and a discussion and conclusions section.17 with a viscous-plastic sea ice component18. We use a standard viscous-plastic rheology19 in our simulations that relates the sea ice strain rates, concentration, and thickness to the internal stresses. The ice strength depends linearly on the ice thickness and exponentially on ice concentration and consequently the sea ice state is a controlling factor in the generation of LKFs. We use an Arctic-wide grid at a horizontal resolution of ∼4.5 km with 50 vertical layers in the ocean component, spanning one face of the cubed sphere, centered on the North Pole. The open boundaries in the Pacific and the Atlantic are prescribed using monthly mean data taken from a global simulation20. Atmospheric forcing fields are taken from either the ERA-Interim Reanalysis21 or from individual members of the ECMWF Ensemble Prediction System (EPS)22 as detailed below.Our study is based on simulations with the Massachusetts Institute of Technology general circulation model (MITgcm)23. Note that by perturbing thickness only, we leave the initial locations of LKFs essentially unchanged.A control simulation from 2001 to 2015 forced with ERA-Interim reanalysis data provides initial conditions for all ensemble prediction experiments. Two sets of 15-member ensemble prediction experiments are considered: In the first set (AtmU hereafter), we account only for atmospheric forcing uncertainty associated with the chaotic atmospheric nature: Different sea ice-ocean model realizations are driven with different members of the ECMWF Ensemble Prediction System using the same initial conditions for the sea ice-ocean system. In the second set (AtmU + IcU hereafter), we additionally take into account the uncertainty of the initial conditions for sea ice. This is achieved by perturbing the initial sea ice thickness fields To reduce the impact of sampling uncertainty, each of the two ensemble experiments was conducted for six different cases by initializing forecasts on the first and fifteenth of the months February, March, and April in 2005. We focus our analysis on February, March and April, when the LKF density distributions are relatively homogenous, noting that LKF predictability might be different in other seasons. We focus on the months around the maximum ice extent because (i) a continuum sea ice model with a 4.5-km grid spacing is thought to best represent a dense ice pack with large floes, (ii) a larger area exhibits LKFs in winter and spring compared to summer and autumn, and (iii) this avoids difficulties in terms of interpretation that might arise from the seasonal cycle in LKF characteristics. The year 2005 was chosen because at that time the operational EPS was technically very similar to the ERA-Interim reanalysis system. Note that for each of the configurations and each of the starting dates, fifteen ensemble members were integrated for ten days (i.e. 10-day forecasts).potential predictability, which typically is regarded as an upper bound for the skill that can be achieved in real operational forecasts4.We follow a “perfect-model” approach in the sense that we do not quantify forecast errors with respect to actual observations, but study how differences between individual ensemble members evolve, with one of the ensemble members being considered the “truth”. Our analysis thus provides an estimate of 24. Sea ice shear and divergence also correspond to leads25. Various methods have been proposed to detect geomorphological features in sea ice over the past two decades, but there is no unique detection algorithm29. The most straightforward approach is to use a fixed threshold for distinguishing linear (kinematic) features from background fields30. Obviously, the result depends on the threshold value. Given that zones of shear as well as convergence and divergence of sea ice are concentrated along LKFs, we opt for a simple method that is based solely on the total deformation rate of sea ice: Regions with sea ice concentration below 80% are excluded from the analysis. After taking the logarithm of total deformation, we subtract the local background which is determined by applying a 2D Gaussian filter with a standard deviation of five grid cells (∼20 km). Grid cells are considered to belong to an LKF if total deformation exceeds the background deformation by at least a factor of Our analysis requires an algorithm that diagnoses linear kinematic features (LKFs) from model output. It is well established to derive LKFs from sea ice deformation fields. The LKFs, indentified in this way, are highly correlated with the spatial pattens of leads1.6 Fig. . Note th31]. Applied to a pair of LKF patterns, the MHD determines the average distance between any LKF grid cell to the closest such grid cell in the other pattern. In contrast to the spatial correlation, which strongly penalizes even small errors in the predicted location of the LKFs, the MHD can discriminate between small and large differences in LKF location. Given that MHD is constrained to binary fields, we use it to determine the predictability of LKFs only. In contrast, spatial correlations are determined for LKFs as well as several continuous fields including sea ice total deformation anomaly, sea ice concentration anomaly, and the divergence of near-surface winds.To quantify the difference between pairs of ensemble members (i.e. “forecast error”), we use the simple spatial Pearson correlation as well as the more sophisticated Modified Hausdorff Distance  for our ensembles. Averaged over the experiments AtmU and AtmU + IcU and normalised with the climatological IIEE, the corresponding predictability after 10 days is 0.72, which is marginally higher than the predictability of sea-ice concentration anomalies of 0.69  such as leads are much less well predictable, especially beyond the first 2–4 days of the forecast. In addition, a skillful prediction of actual LKFs is a challenging task that requires a sophisticated forecasting system with new data assimilation capabilities to obtain accurate initial conditions for sea-ice failure12.Experiments with a high-resolution sea ice-ocean model, forced with weather forecasts from the ECWMF ensemble prediction system, have been used to determine the potential predictability of Arctic sea ice on “weather time scales” (1–10 day forecasts), a time range for which skilful forecasts are of critical importance for marine stakeholders in tactical decision making. Our study augments previous work, most of which focussed on longer-term seasonal and interannual predictionOur results suggest that atmospheric uncertainty is more important in generating sea ice forecast error than initial sea ice uncertainty. In this context, we have conducted additional experiments in which we varied the parameters of the initial perturbations. From these experiments we conclude that smaller-scale perturbations introduce spurious additional LKFs, and that the chosen set of parameters appears appropriate. Still, it cannot be excluded that our initial sea ice perturbations are sub-optimal, in the way they reflect real uncertainties and in the way they project onto possible directions of growing forecast error. However, our results are consistent with the notion that sea ice dynamics are primarily determined by atmospheric winds. The higher predictability of wind divergence in the central Arctic Fig.  might al35. Furthermore, estimates of deterministic and probabilistic predictability may differ. Therefore, deterministic verification of forecasts, as done in this study essentially by comparing pairs of ensemble members, should be augmented by probabilistic verification where ensemble-derived probabilities are taken into account.Our study shows that the verification of forecasts of small-scale sea ice features (including determining exact limits of predictability) poses challenges. Traditional measures such as the anomaly correlation coefficient, for example, are quite sensitive to small errors in location. Hence, other less well-established methods such as Modified Hausdorff Distance need to be exploited as well when it comes to user-relevant forecast verification. Future work on sea ice verification can benefit from the expertise gained from the verification of other sharp features such as atmospheric fronts36 or an ice thickness distribution38 open new fields of application in the context of predictability. With our current model configuration, these questions cannot be addressed.Individual LKFs may not be the only important predictands for shipping or marine operations. Integral properties such as ridge and lead density may also be relevant and probably easier to predict but would require a different type of analysis and potentially different model parameterizations. For example, a ridge density parameterization39 and only partly for models at higher grid resolution like the one used here15. In this light, the potential predictability reported here may be actually slightly overestimated. Repeating our experiments with a new generation of rheologies that produce more intermittency in models42 may provide enhanced estimates of Arctic sea ice predictability on weather time scales.Simulated small-scale sea ice features depend heavily on the rheology used for the representation of sea ice dynamics. It seems natural, therefore, to ask how model dependent the results from this potential predictability study are. The high intermittency of sea ice deformation found in temporal scaling analyses of drift observations could not be reproduced with viscous-plastic sea ice models at coarse grid resolutions


Among its regulators, protein phosphatase-1 (PP1), a Ser/Thr phosphatase, appears to be critical in controlling CaMKII-dependent neuronal signaling. In postsynaptic densities (PSDs), CaMKII is required for hippocampal long-term potentiation (LTP), a cellular process correlated with learning and memory. In response to Ca2+ elevation during hippocampal LTP induction, CaMKIIα, an isoform that translocates from the cytosol to PSDs, is activated through autophosphorylation at Thr286, generating autonomous kinase activity and a prolonged Ca2+/CaM-bound state. Moreover, PP1 inhibition enhances Thr286 autophosphorylation of CaMKIIα during LTP induction. By contrast, CaMKII nuclear import is regulated by Ser332 phosphorylation state. CaMKIIδ3, a nuclear isoform, is dephosphorylated at Ser332 by PP1, promoting its nuclear translocation, where it regulates transcription. In this review, we summarize physio-pathological roles of CaMKII/PP1 signaling in neurons. CaMKII and PP1 crosstalk and regulation of gene expression is important for neuronal plasticity as well as survival and/or differentiation.Ca In the brain, Ca) kinase , regulatasticity . CaMKII asticity ,5.2+ elevation by extracellular stimuli, Ca2+-CaM binding to CaMKII displaces autoinhibitory domains to allow ATP and exogenous substrates access to the active site. Immediately after activation, Thr286 in the autoinhibitory domain of the α isoform  is autophosphorylated by the neighboring kinase domain. This event increases Ca2+-CaM binding affinity and blocks interaction of autoinhibitory and catalytic domains, thereby generating autonomous kinase activity and prolonging the Ca2+/CaM-bound state. CaMKIIα autonomy is critical for induction and maintenance of hippocampal long-term potentiation (LTP), both of which underlie learning and memory [CaMKII has attracted substantial attention due to its function in synaptic plasticity, an activity that occurs at postsynaptic densities (PSDs) ,7,8. In d memory ,10.2+ stimulus, two adjacent CaMKII monomers are simultaneously bound by Ca2+/CaM. In these conditions, one subunit serves as a substrate for the other, resulting in Thr286/Thr287 phosphorylation. Once the first subunit is phosphorylated, subsequent phosphorylation within the holoenzyme is more likely to occur, as lower Ca2+ levels are required for the second phosphorylation. Thus, in this scenario, CaMKII remains active, even when Ca2+ levels return to basal levels, until it is dephosphorylated. If the number of phosphorylated subunits exceeds a threshold and the phosphorylation rate is greater than the dephosphorylation rate, then CaMKII activity is sustained [The 12 subunits of the CaMKII holoenzyme assemble into two coplanar rings, each containing six subunits . These rustained .328KKRKS332), is reportedly phosphorylated by the CaMK family members CaMKI or CaMKIV, blocking association of CaMKII with the NLS receptor m-pendulin and thereby preventing nuclear localization [In contrast to postsynaptic CaMKII function, the physiological relevance of nuclear activity of CaMKII isoforms in the central nervous system (CNS) remains unclear. Alternative splicing of CaMKII generates a multitude of isoforms for each CaMKII subunit . Among tlization .The Ser/Thr phosphatase Protein Phosphatase-1 (PP1), a key regulator of CaMKII signaling, forms a heterodimer comprised of a catalytic (PP1c) and a regulatory subunit. PP1c can form a complex with over 50 regulatory or scaffolding proteins that dictate substrate specificity and subcellular localization . In mammIn this review, we focus on the role of CaMKII/PP1 signaling in both neuronal plasticity at PSDs and gene expression in the nuclei. We also discuss how imbalanced CaMKII/PP1 activity may underlie neuronal pathologies, such as mental disorders and neurodegeneration.PSDs are localized in the tips of dendritic spine heads and contain multiple classes of proteins that function in neuronal signaling in response to presynaptic neurotransmitter release, such as glutamate . CaMKII One factor governing this persistent “on-state” is PP1 localization to dendritic spines and PSDs ,33. PP1 Once activated, CaMKII remains in an active conformation throughout the LTP maintenance phase, an observation that forms the basis of the hypothesis that CaMKII is critical for memory formation ,10. HoweIn animal models of Parkinson’s disease, striatal dopamine depletion increases CaMKIIα autophosphorylation at Thr286 in parallel with decreased PP1γ1 activity and increased PP1γ1 binding to spinophilin ,43,44. M2+ signaling, and nuclear Ca2+ transients are some of the most potent regulators of neuronal gene expression [Transduction of signals from synapses to the nucleus is primarily mediated by Capression . Nuclearpression ,49 throupression ,51, methpression , activatpression ,54, CCAApression ,56, and pression .2+-induced CaMKII activation in primary cultured neurons stimulates CREB phosphorylation at Sers 133, 142, and 143 [2+-induced CREB-dependent transcription [2+ influx trigger CaMKII-dependent MeCP2 phosphorylation at Ser421 [Specifically, CaMKII phosphorylates CREB at Ser133 and Ser142 in vitro . Moreove and 143 . CREB phcription . Howevercription . The tracription . Neuronat Ser421 . Knock-it Ser421 ,62, unde328KKRKS332), is phosphorylated by CaMKI or CaMKIV, prohibiting nuclear localization [Until recently, mechanisms underlying substrate phosphorylation by nuclear CaMKII remained unclear. Thus, we investigated nuclear-cytoplasmic shuttling of the nuclear isoform CaMKIIδ3. Previously, others had reported that CaMKIIδ3 Ser332, which is C-terminal to the NLS (lization . To inve2+/CaM to the nucleus following depolarization of cultured superior cervical ganglion neurons and that the Ca2+/CaM-CaMKIIγ complex is dephosphorylated at Ser334 by calcineurin, allowing it to shuttle to the nucleus. Nuclear delivery of Ca2+/CaM activates nuclear CaM kinases, including CaMKIV and CaMKK, driving CREB phosphorylation and transcription of its target genes [Others have reported nuclear activity of CaMKIIαB and CaMKIIγA in neurons ,64. For CaMKII-PP1 signaling transcriptionally regulates BDNF, a factor vital for neuronal survival, growth, and maintenance, in brain circuits functioning in emotion and cognition . MeCP2 mWe also previously revealed that nuclear CaMKII/PP1 signaling is important for neuronal survival and differentiation . We repo2+ signaling to PSDs or nuclei. However, the composition of the dodecameric CaMKII holoenzyme affects CaMKII localization [CaMKII/PP1 signaling plays a crucial role in many different aspects of synaptic plasticity in PSDs and in activity-regulated transcription in nuclei. CaMKII alternative splicing generates numerous subtypes of each CaMKII isoform. lization ,79. The lization . Nuclearlization  and/or Flization  to facil


This study compares the cyclic loading properties and failure loads of two screw combinations on a synthetic Schatzker type 1 tibia fracture model. Our hypothesis was that after adequate compression with first a partially threaded screw, addition of a fully threaded screw would provide more stability than an addition of a second partially threaded screw.n = 10) with two partially threaded screws and in group B (n = 10) with one fully threaded screw and one partially threaded screw. Load-displacement evaluation was made at each 1000-cycle interval up to 10,000 cycles. Failure load was identified as the load creating a 2-mm displacement. Two-factor (groups and periods) repeated measurement analysis of variance and independent sample t tests were used.The Schatzker type 1 tibial plateau fracture model was created. Fixation was obtained in group A (p = 0.29) and time-period interaction (p = 0.59) . In the test of between-subject effects, there was no significant difference between groups in terms of cyclic loadings . However, in the t test for each 1000-cycle interval, the value of mean displacement in group B was significantly lower than that in group A in the initial, 1000-, 2000-, and 3000-cycle intervals . The mean displacement and standard deviations increased with the number of cycles. The mean range of displacement initially was 0.66 mm for group A and 0.36 mm for group B. The mean range of displacement after 10,000 cycles was 0.79 mm for group A and 0.44 mm for group B. The mean failure load value was 682 ± 234 N for group A and 835 ± 245 N for group B. In independent sample t tests, there were no significant differences between the two groups in terms of failure load (p > 0.05).According to the two-factor repeated analysis, there was no significant difference for periods (Obtaining fixation with one partially and one fully threaded screw can minimize displacement at the fracture site at early cyclic loadings. The treatment goal for tibial plateau fractures is anatomic reduction and rigid fixation to allow early mobilization. Several fixation methods have been described for rigid osteosynthesis. Percutaneous fixation using a partially threaded cannulated screw is one of the methods, which is reported by some authors with a high rate of success –3. In thThe cyclic loading properties and failure loads of screw combinations with different thread lengths were compared biomechanically on a Schatzker type 1 tibia fracture model using synthetic bone.Twenty identical synthetic right tibia bones representing healthy bones without osteoporosis  were used in the study. According to the manufacturer, although their synthetic tibia model is not as strong as the natural bone, it had very favorable feedback from biomechanical testing facilities and it was demonstrated with ballistic testing that it fractures in a manner very similar to the live bone [In order to get standardized results, we needed to standardize screw placement and osteotomy sites. To standardize screw drill holes, first, we drilled two holes in a tibia from lateral to medial with 4.5-mm drill bits, as we would normally do in routine surgeries of Schatzker type 1 tibial plateau fractures. Then, without removing the drills from the bone, we placed them in a durable card box and poured the putty so as to completely encase the bone and the drills. As the polyester steel putty hardened it took the shape of the materials. Then, we pulled out the drills from the bone. Because the putty also hardened around the drills, the location, size, and direction of the drill holes could now be replicated with exact accuracy. This way, when we put a new tibia in the hardened putty template, drills would go through the tunnel inside the putty and we could target them into the bone in a standardized manner Fig. .Fig. 1aTo create an osteotomy template, we first created a template of the proximal tibia by using the same method of encasing the bone about halfway inside the putty and letting it harden around the bone. We decided to create an osteotomy line 1.5 cm from the lateral edge of the tibial plateau. Then, we sliced the template in a way so that when the bones would be later placed on it, part of the tibia to be osteotomized would hang at the side of the template Fig. . In thisAfter a model was osteotomized, the fragment was repositioned and fixed with combinations of two group screws. All screws were 75 mm in length and 6.5 mm in diameter. Two partially threaded cancellous screws were used for group A and one partially threaded screw and one fully threaded screw was for group B . The partially threaded screws in group B were always placed in the posterior hole, and the fully threaded screws were always placed in the anterior hole. Before the screws were placed, the screw path was tapped and they were tightened with screwdrivers at a torque limit of 1.5 N. This limit was chosen to make sure that all the screws were tightened with the same amount, and the screwdriver with 1.5-N limits is used routinely in our daily practice. Tibia specimens were cut perpendicularly to the long axis of the bone at 13 cm of the distance from the proximal end. Each sample was embedded in a plastic cylinder, which was 5 cm in diameter and 5 cm in length, using polyester steel putty. This was done so that the distal end of the tibia would be suitable for adapting on a testing machine. X-rays of the specimen are shown in Fig. The material test device (MTS 858 Mini Bionix™ II) was set up in such a way as to have direct loading on the fracture fragment Fig. . This siA load to failure test was performed as a preliminary test on a sample from each group to identify the load to be used in cyclic loading tests. The lowest failure load among samples was selected, and two thirds of this value, 300 N, was used as the upper limit in the cyclic loading tests. For the cyclic loading, frequency and preload were determined as 5 Hz and 10 N, respectively. Between each 1000-cycle interval, the image analysis software recorded a load-displacement evaluation under 300 N of load. This load was not applied instantly but incrementally in 30 s with an increase of 10 N/s to detect movement with better accuracy. After 10 cyclic loading intervals, failure load values were obtained by continuous loading. The number of cycles was determined as 10,000 cycles according to similar studies in the literature , 16, 17.Following the cyclic loading, a load to failure test was performed. The loading speed was identified as 5 mm/min, and the loading was maintained until 3 mm of displacement. The displacements were detected with an optical camera during this loading phase. The load that caused a 2-mm displacement was identified as the failure load. In one of the load to failure tests, technical errors occurred and meaningful values were not obtained. This measurement was excluded from the study. An example load-displacement graphic, consisting of an initial and 10 cyclic intervals and load to failure tests, is demonstrated in Fig. t tests were used to analyze each period and failure load. Two-factor (groups and periods) repeated measurement analysis of variance was used to simultaneously analyze all cyclic loadings. A p value <0.05 was considered as significant. All statistical analyses were performed using SPSS software  on a personal computer.Independent sample t test, the value of mean displacement in group B was significantly lower than that in group A in the initial, 1000-, 2000-, and 3000-cycle intervals . The mean range of displacement initially was 0.66 mm for group A and 0.36 mm for group B. The mean range of displacement after 10,000 cycles was 0.79 mm for group A and 0.44 mm for group B. The mean displacement and standard deviations increased with the number of cycles . The mean and p values are shown in Table The second statistical analysis concerned differences of mean failure loads to assess the strength of the fixation. The mean failure load value was 682 ± 234 N for group A and 835 ± 245 N for group B. There were no significant differences between the two groups in terms of failure load in independent sample p = 0.29) and time-period interaction (p = 0.59) . In the test of the between-subject effects, there was no statistical difference between the groups as far as how they effect displacement by themselves .Finally, repeated measurement analysis was performed to make a comprehensive evaluation of our data, which included all cycles. This analysis was made using two factors. Of these two, the within-subjects factor was periods of each 1000 cycle and the between-subjects factor was groups. According to the multivariate tests, there was no significant difference for periods (The tibiofemoral joint is subject to different types of forces in different daily activities. The reason of the failure of plateau fractures of the tibia is primarily shearing forces, which cause the fracture fragment to displace, resulting in failure of the osteosynthesis. Therefore, we preferred to test our hypothesis under conditions that create shearing forces on the tibiofemoral joint. The shearing forces are different in various daily activities. They also change with the degree of flexion and extension in the knee joint. The shearing forces are greatest at the last few degrees of extension , 18. ThiCyclic loading test is very important for representing physiological loadings , 19. We The lag technique is still a recognized and accepted technique for optimal primary healing. According to this technique, all threads must cross the osteotomy or fracture site to generate compression. We could not find a paper analyzing different thread configurations at the fracture line in terms of biomechanical features. Our results indicate that after adequate compression with first a partially threaded screw, using a fully thread screw may increase the initial stability of fracture fixation. The loss of the significant differences after 3000 cycles can be explained by the loosening of the screws after cyclic loading for both groups.While no studies on plateau fractures comparing screw combinations exist, there are some similar studies in the literature, although they do not compare fully and partially threaded screws . FixatioThe failure load-displacement amount was chosen as 2 mm because 2 mm is a common threshold for treating intra-articular fractures operatively. Furthermore, the number of cycles in this study can be considered low, especially compared to the standard steps/day values required for non-sedentary lifestyle in healthy adults . For a nDue to its experimental nature, there are several limitations to our study. The first and foremost limitations of this study were the low number of samples and the use of synthetic bone instead of real bone. Another limitation was that we chose to stick our grids right across the midpoint of the fracture line. However, the screws across the fracture line can act as a center of rotation under a lateral shearing force, causing the proximal and distal parts of the fracture to displace differently. Therefore, this study treats the fracture as a single rigid body that shows constant displacement at different corresponding points, which is a simplification of the real fracture mechanics. However, we believe that, although a limiting factor, the difference can be negligible for this sample size.In real life, the force applied by the body weight on the tibia changes with each degree of flexion and extension and the force applied on the joint surface is different at each point along the gait cycle. However, due to practical limitations, our setup was prepared in such a way as to be an approximation by accepting the load to be constant along the cycle. Furthermore, in real life, the load applied on the joint surface is not the same at every point. However, we could not factor that in our experiment because the cyclic load was applied not by a distal femur as in real life but through a sheet-like hard rubber, which had a smaller surface area than the joint surface. Therefore, the load was concentrated on a smaller area. Since we intend to imitate a real-life biomechanical process, this is a major limitation of our study. The sheet-like rectangular rubber used to distribute the force applied by the machine on the tibia models had a hard consistency to transmit the force without much compression. Still, some amount of the force applied may be lost in compressing the rubber. Insignificant as it may be, it can be considered as a systematic error in our study. In the light of these limitations, we believe further studies with a large number of samples—preferentially real bone—are needed.Obtaining fixation with one partially and one fully threaded screw can minimize displacement at the fracture site at early cyclic loadings. This combination might be useful for the Schatzker type 1 tibial plateau fracture during the early rehabilitation period. Although we have failed to demonstrate significant differences at later cycles and failure loads, we have successfully demonstrated that different screw combinations may have different biomechanical properties at the fracture site. With further research, this fact may be translated into meaningful clinical results by adapting specific screw combinations depending on the type of fracture to achieve more secure fixation.