Starch is widely used as an ingredient and significantly contributes to texture, appearance, and overall acceptability of cereal based foods, playing an important role due to its ability to form a matrix, entrapping air bubbles. A detailed characterisation of five gluten-free starches  was performed in this study. In addition, the influence of these starches, with different compositional and morphological properties, was evaluated on a simple gluten-free model bread system. The morphological characterisation, evaluated using scanning electron microscopy, revealed some similarities among the starches, which could be linked to the baking performance of the breads. Moreover, the lipid content, though representing one of the minor components in starch, was found to have an influence on pasting, bread making, and staling. Quality differences in cereal root and tuber starch based breads were observed. However, under the baking conditions used, gluten-free rendered wheat starch performed best, followed by potato starch, in terms of loaf volume and cell structure. Tapioca starch and rice starch based breads were not further analysed, due to an inferior baking performance. This is the first study to evaluate gluten-free starch on a simple model bread system. Starch is the primary energy storage compound in many plants including cereals, legumes, potatoes and tubers and provides 70%–80% of the calories consumed by humans worldwide . Starchei.e., they swell and are partially solubilised, but still maintain their granular identity [The main starch sources in gluten-free systems are corn, tapioca, potato, rice, and gluten-free wheat . Native identity . Starch identity . Moreoveidentity .et al. [Gluten-free products are important for people who suffer from coeliac disease (CD), an immune mediated enteropathy causing inflammation in the small intestine . It is tet al. . ModificThe present work is novel in its use of a simple gluten-free model formulation (seven ingredients), enabling the examination of the bread making performance of different starches. It aims to deepen the knowledge on how different starches influence baking performance. This is accompanied by a wide range of analyses characterising the gluten-free starches.The suppliers for the ingredients used were Cargill, UK for corn starch; Agrana, Austria for potato starch; Roquette, France for gluten-free wheat starch; Tradelink, UK for tapioca starch and Beneo Remy, Belgium for rice starch. Dry yeast was supplied by Puratos, Belgium; sugar from Siúcra Nordzucker, Ireland; salt from Glacia British Salt Limited, UK and hydroxypropyl methylcellulose (HPMC) from J. Rettenmaier, Germany.Samples of starch were dried in an air-oven for 1 h at 103 °C. Samples were affixed with double-sided carbon tape to an aluminium stub and coated with a layer of 25 nm of sputtered palladium-gold. Hereupon, samples were examined under high vacuum in a field emission scanning electron microscope (SEM) with a working distance of 8 mm. Secondary electron images were acquired at an accelerating voltage of 5 kV. For processing of the images SEM Control User Interface software, Version 5.21  was used.Analysis of the particle size distribution was carried out by laser diffraction using a dry feed cell . The sample was dispersed in air using 1.5 bar pressure and measured when an obscuration of 5%–7% was achieved, with a refractive index of 1.45.Chemical characterisation of the starch samples was performed using laboratory standard methodologies. Moisture determination was performed using the air oven method (AACC Method 44-15 A). Total starch (AACC Method 76.13), damaged starch (AACC Method 76-31.01), amylose/amylopectin content of total starch, α-amylase activity (AACC Method 22-02.01) and β-amylase activity were determined utilizing commercially available assay kits . The protein content was determined using the Bradford assay with bovine serum albumin as the calibration standard. For the determination of the lipid content, the starch samples were first digested according to the Weibull-Stoll method, to release bound lipids. The lipid content was then determined using AACC Method 30-25.01.w/ww). The temperature profile used was heating to 95 °C (6.3 °C/s), holding at 95 °C for 162 s and cooling to 50 °C (5.1 °C/s) and holding at 50 °C for 120 s.The pasting properties of the starch samples were determined according to the Newport Scientific Method 6, Version 4, December 1997, using a RVA Super 3 Rapid Visco Analyser  with thermocline control and data collecting software. Starch sample of 3 g (on a basis of 14% moisture) were weighed into an RVA aluminium canister, and 25 g of distilled water were added, to prepare a starch-water sample of 9.2% . For the pre-fermentation, yeast was suspended in warm water (25 °C) and regenerated for a period of 10 min. Mixing was carried out with a k-beater  at low disk speed (level 1 of 3) for 1 min in a Kenwood Major Titanium KM 020 Mixer . After that the dough was scraped down from the bowl walls and a further mixing of 2 min at higher disk speed (level 2 of 3) was carried out. The batter was scaled to 300 g into nine baking tins of 16.5 cm × 11 cm × 7 cm and placed in a proofer for 45 min at 30 °C and 85% relatively humidity (RH). The breads were baked for 55 min at 220 °C top and bottom heat in a deck oven, previously steamed with 0.7 L of water. After baking, bread loaves were removed from the tins and cooled at room temperature for 2 h. The loaves were subsequently analysed or packaged in plastic bags  for storage. Each starch bread batch was prepared in three replicates.The bread analyses were performed 2 h after baking. Four of eight loaves were immediately used for performing the texture and structural analyses on day 0, the remaining four loaves were used for the texture analysis on storage day 2 (50 h after baking) and storage day 5 (122 h after baking), two each day. For the analysis performed on day 2 and 5, the bread samples were packaged in polythene bags  and stored at room temperature. Specific volume was determined using a Vol-scan profiler , equipped with a non-contact measurement device. The bake loss was measured as the difference between the initial weight of the sample (dough in the tin before fermentation) and the final weight of the sample (bread after baking and cooled for 2 h). For measuring the moisture of the bread crumb an air oven was used according to AACC Method 44-15A. The texture profile analysis (TPA) of the bread samples was determined using a universal testing machine TA-XT2i . This measurement, also called the “two-bite-test”, evaluated seven parameters assigning independent numeric values to bread attributes normally estimated with sensory test. For analysing the texture changes over times, the measurement was done on day 0 (the baking day), day 2, and day 5. On the baking day, two breads were sliced transversely by using a slice regulator and bread knife to retain consistent slices of 20 mm thickness. Eight bread slices, taken from the center of the breads, were used to evaluate the texture parameters using a universal testing machine TA-XT2i equipped with a 25 kg loading cell and a 35 mm aluminium cylindrical probe. The settings used were a test speed of 5 mm/s with a trigger force of 25 g to compress the middle of the bread crumb to 50% of its original height. The measurement was repeated after 2 and 5 days of storage using the bread samples packaged in a plastic bag and stored at room temperature. For crumb grain evaluation a C-cell Bread Imaging system  equipped with C-Cell version 2.0 software was used. The area of holes  and the wall thickness  were chosen to describe the crumb grain.r) and p-value were used to show correlations and their significance using the Basic Statistics package of the software MINITAB version 15 . Differences of p < 0.05 were considered significant. The correlation coefficient is classified in different levels of correlation: perfect (|r| = 1.0), strong (0.80 ≤ |r| ≤ 1.0), moderate (0.50 ≤ |r|≤ 0.80), weak (0.10 ≤ |r| ≤ 0.50), and very weak  correlation (|r| ≤ 0.10).All measurements were performed at least in triplicate. The confidence interval was calculated and the results were checked using the Grubbs-test (outlier-test) with a significance level of α = 0.05. Pearson correlation coefficient (SEM was used to compare the microstructure of the different starches. Different granule sizes (A and B granules), shapes and the mix of different size granules in the starch have an influence on the rheology and the functional and structural properties of starch based foods . The morThe granular size of the different starches was determined by laser diffraction using a dry feed cell. The results are shown in 32) showed the following order, starting with the biggest one: potato starch (36.7 μm), gluten-free wheat starch (18.9 μm), tapioca starch (18.1 μm), corn starch (12.9 μm), and rice starch (12.3 μm). However, the size distribution showed that rice starch had high values, similar to potato starch. This is due to the agglomerated state of rice starch, which leads to a detection of big granules  contained a lower moisture level compared to the tuber starches (potato and tapioca starch). The total starch contents were, except for tapioca starch and corn starch, significantly different. From the starches analysed, gluten-free wheat starch showed the highest, and rice starch the lowest, total starch content. As expected, for the amylose and amylopectin contents significant differences between the starches were found. The potato starch had the highest amylose content (54.2%), while gluten-free wheat starch had the lowest (25.1%). Properties such as susceptibility to enzymatic hydrolysis and the gelling and pasting behaviour can be related to the amylose content. Furthermore, a higher content of damaged starch was found for the cereal starches. These differences were also detected by Schirmer et al. . The higet al. . The lipet al. . Howeveret al. . It can  [et al. , who ana [et al. . The eff [et al. . These e [et al. . Hence,  texture . This exAlpha-amylase is an important enzyme occurring in different starch sources . It is aThe pasting properties of the different starches were determined with the rapid visco analyser (RVA), an instrument that measures the viscosity (cP) of a sample while a specific temperature profile is applied. et al. [et al. [r| 0.94). In general, it had been found that large granules  have a greater swelling capacity and therefore form highly viscous pastes [et al. [The differences in pasting behaviour between the cereal and root/tuber starches are influenced by the different ratios of amylose and amylopectin in starch, which cause different degrees of gelatinisation. Starch gels are an amylose network, where swollen granules are immersed . Furtheret al.  demonstret al. . Sánchez [et al.  stated ts pastes  and are  [et al. , to haveIn this study it was not possible to correlate the pasting behaviour of a starch to a single factor. The authors hypothesize that the pasting properties are dependent on many intrinsic and extrinsic factors instead of one single factor. This is in agreement with Abdel-Aal , who repPictures of the model breads shown in r| 0.92). This is in agreement with Park et al. [r| 0.83), which in turn led to the high bake loss (|r| 0.89) of corn starch; due to the greater surface of the loaf, more water was able to evaporate. Rice starch and tapioca starch produced breads with an irregular structure and large holes. The rice and tapioca starch based breads could not be used for further study due to their poor structure. The SEM pictures, shown in The analysis and correlation of starch characteristics with bread structure revealed that starches containing A and B granules resulted in the best bread structure , which results in higher swelling power and the granule size. This is in agreement with literature [et al. [et al. [et al. [Potato starch bread showed significantly higher hardness values in comparison to the cereal starches. Correlation analysis showed that these results are linked to its high amylose content  and swelling power (|r| 0.92) and lipid content (|r| −0.71). Potato starch contained the highest amylose content , specific volume (|r| −0.93), crumb moisture (|r| 0.99), and the staling rate (|r| 1.00). For the baked bread analyses, the rice starch and tapioca starch were excluded due to their lack of a bread structure. It is suggested that the high β-amylase activity and the high damaged starch content in rice starch lead to this weak performance. Although the results of the characterization of potato and wheat starches showed no similarity (except morphology) between them, the resulting bread structure was very similar. Overall, this study showed in a model bread system that gluten-free wheat starch is the best option for the production of gluten-free bread followed by potato starch in terms of volume and bread structure. This study contributes to the knowledge of gluten-free baking by highlighting the differences of various starches in a simple model bread system. The correlation between granule size and baking characteristics further supports the idea that larger granules are better suited to gluten-free bread production. Although the morphology of the starches has a major impact on the final product, the differences in the composition of the starches should not be neglected. Therefore, further research on the effect of starches from the same source, but of different composition could give further insights into the importance of starch source or composition. In addition, further research on interactions between different components and their behaviour in a model bread system could provide a deeper understanding of gluten free systems and help to gain a fundamental understanding of how wheat flour can be replaced by gluten-free ingredients.Correlation analysis revealed that the granule size of the starches has the highest impact on bread texture and structure. It correlated with the bake loss (|


Alloy and microstructure optimization have led to impressive improvements in the strength of engineering metals, while the range of Young’s moduli achievable has remained essentially unchanged. This is because stiffness is insensitive to microstructure and bounded by individual components in composites. Here we design ultra-low stiffness in fully dense, nanostructured metals via the stabilization of a mechanically unstable, negative stiffness state of a martensitic alloy by its coherent integration with a compatible, stable second component. Explicit large-scale molecular dynamics simulations of the metamaterials with state of the art potentials confirm the expected ultra-low stiffness while maintaining full strength. We find moduli as low as 2 GPa, a value typical of soft materials and over one order of magnitude lower than either constituent, defying long-standing composite bounds. Such properties are attractive for flexible electronics and implantable devices. Our concept is generally applicable and could significantly enhance materials science design space. The rule of mixtures usually causes composite properties to fall between the maximum and minimum of the parent phases. Here, the authors use large-scale molecular dynamics simulations to break that rule by stabilizing a negative stiffness state in fully dense nickel-aluminum nanowires to achieve ultra-low stiffness. Nature also uses composites to achieve outstanding mechanical performance by arranging relatively humble materials in optimized microstructures3; classic examples include nacre4, bone5, and spider silk6. Unfortunately, the design of composites is not without limitations. With few notable exceptions, their properties fall between those of its constituents and various forms of the rule of mixtures play a central role in composite design. The weighted average rule applies rigorously for properties like density where the value for the composite can be obtained as a sum over phases: xi is the volume fraction of phase 7. Such properties are bounded by extrema obtained by the addition of phases in series or in parallel. This is true even as composite, foam, and lattice material design has successfully filled previously inaccessible materials property space8, for example, ultra-low stiffness metallic micro-lattices10 and structures with negative Poisson’s ratio12.Engineered composites combine distinct materials to achieve properties or processability not available in the individual components and find widespread use, from advanced airframes14 and composites designed for low thermal conductivity15. Intrinsic properties have proven more challenging. We mentioned that the weighted average rule applies exactly to density and has, to date, provided inexorable bounds on stiffness. Even the recent, remarkable observation of stiffening of a solid via nanoscale liquid inclusions can be explained by classic composite theory after the incorporation of interfacial tension16.There is significant interest in breaking rule of mixtures constraints to reach otherwise unachievable properties. Breakthroughs in materials science and engineering have enabled overcoming such constraints for extrinsic properties, those that depend strongly on microstructure. Examples include biomimetic composites with high toughness17 show a stiffness over one order of magnitude smaller than that of its softest component. In absolute terms, we show a fully dense metal with a stiffness of as low as 2 GPa, a value typical of polymers. This unprecedented result is possible because one of the components in the metamaterial is stabilized in a thermodynamically forbidden state of negative stiffness by interfacial stresses originating from its coherent integration with a thermodynamically stable material. This idea of harnessing unstable states has been demonstrated experimentally using ferroelectrics in nanoelectronic applications19 and to achieve both ultra-high damping21 and ultra-high stiffness22 composites. While these studies have produced impressive properties, most are dynamic and the displayed properties are transient; ref. 18 is an exception for electronic properties. In contrast to prior efforts in mechanical properties, the metamaterials described in this paper stabilize a negative phase, resulting in ultra-low stiffness under quasi-static conditions.In this paper, we use theory to design an ultra-low stiffness metamaterial, a composite that defies the constraints of the rule of mixtures for a quasi-static property weakly sensitive to microstructure: stiffness. We note that stiffness depends on microstructure (via texture), but to a much lesser degree than strength and toughness, and can to date only be engineered to a large extent via open structures such as foams and lattices, which incorporate air as a second phase. Molecular dynamics (MD) simulations that explicitly model the mechanical metamaterial23 and martensitic transformations that govern shape memory and superelasticity25, as well as strengthening in some advanced alloys27. We harness martensitic transformations, solid–solid phase transformations where a material switches between a high-temperature and high-symmetry (often cubic) phase called austenite to a low-temperature, lower-symmetry phase martensite. The transformation does not involve atomic diffusion and is often accompanied by significant changes in lattice parameters.Materials experience thermo-mechanical instabilities during phase transformations: classic examples are phase separation via spinodal decomposition63Al37 alloy. The details of the model will be described below; we now focus on general aspects of the metamaterial design. The energy landscape exhibits two local minima. One minimum represents the ground state structure, which for this system at room temperature is the martensite; a large energy barrier separates this ground state from a metastable state (austenite in our case). Materials scientists make use of both ground states and metastable phases, the latter being feasible as long as high-energy barriers ensure their long-term viability. In contrast, we are not interested in using either local minima, but instead the states around the local maximum. The local curvature of the free energy vs. lattice parameter represents an elastic constant  and the states around the maximum exhibit negative stiffness. That is, under these conditions the material is at or near a mechanical tipping point. Thermodynamics tells us that such a state is unstable28 and our goal is to harness it to achieve an ultra-low stiffness metamaterial by stabilizing it, barely, at the atomic scale using interfacial stresses originating from its epitaxial integration with a second phase of positive stiffness, represented by the blue line in Fig. The design of ultra-low stiffness metamaterials starts with the free energy landscape that governs a martensitic transformation, that is, free energy as a function of a transformation progress variable. We find it convenient to use a lattice parameter as the progress variable. The red curve in Fig. xAlx1− alloys, which exhibit martensitic transformation and shape memory for x between 60 and 65 at%29. The interatomic potential is a widely used embedded atom model (EAM) developed by Farkas et al.30, which, as in the real case, exhibits martensitic transformation from a B2 (CsCl) to a monoclinic phase in Ni-rich NixAlx1−. The martensitic transformation of our model system involves the expansion of two of the cubic lattice parameters, taken as the order parameter, and the contraction of the third together with a change of the monoclinic angle. To stabilize the Ni63Al37 alloy in its unstable state  with significant stacking faults, see Supplementary Fig. 63Al37 wire begins in the martensitic phase and shows superelastic behavior governed by a change in domain orientation. In order to develop an atomic-level picture of the processes of elastic and inelastic deformation, we analyzed the atomistic trajectories using a common neighbor analysis (CNA), as shown in Fig. 63Al37 and light red in NiAl), while atoms in an austenite environment are colored in shades of blue (dark blue in Ni63Al37 and light blue in NiAl). Stacking faults in the martensite (shown in green) can also be determined from the local bonding environment and are useful in identifying domains as they always lie on {110}B2 planes that contain the short lattice parameter. The initial structure of the Ni63Al37 wire consists of a single martensitic domain /Shell(NiAl) (herein denoted T-Core for transforming core) and Core(NiAl)/Shell(Ni63Al37) (denoted T-Shell) are shown in Fig. 63Al37 shell are identified as austenite and some of the NiAl core as martensite , and showing narrower RDF peaks. Once the change in lattice parameter afforded by the flat region of the free energy landscape is exhausted, the 70 at% T-Shell wire stiffens and achieves an ultimate tensile strength comparable to the pure phases  region of free energy pushes the coherence limit to significantly larger lengths. In fact, Buschbeck et al.40 demonstrated the coherent integration of Fe–Pd magnetic shape memory alloys with a family of substrates spanning the entire Bain path. A process that could affect the properties of the metamaterials is interlayer diffusion. To assess the sensitivity of our results to this process, T-Shell nanowires were created with a graded composition across the interface, see Supplementary Note The possibility of synthesizing structures similar to those studied here and the challenge of epitaxial integration also deserves discussion. Coherent core/shell nanowires2Cu precipitates formed during heat treatment of TiNiCu shape memory alloys have been reported41. The authors demonstrated orientation relationships between the matrix and precipitates during multiple martensite–austenite transformation cycles, significantly improving the fatigue life of the alloy.Finally, we note that our approach does not require core/shell or laminate geometries; coherent precipitates in a matrix are expected to exhibit the same effect. Interestingly, coherent, nanoscale TiIn summary, by harnessing the negative stiffness state of a martensitic material and stabilizing it at the nanoscale via epitaxial integration, we demonstrated an ultra-low stiffness, full density metal. The metamaterial was designed by identifying an appropriate second component capable of stabilizing the negative stiffness region. Large-scale MD simulations show that the flat energy landscape underlying the metamaterial does, in fact, result in ultra-low stiffness. The metamaterial nanowires exhibit Young’s moduli in the low gigapascal regime, similar to that of many polymers, as a wide range of lattice parameters result in nearly identical energies. This softening is achieved at full density and with no loss of strength.43 materials. The results in refs. 22 and related work also harness negative stiffness states; however, in contrast to our work, their systems are not epitaxial and their properties are transient. The authors use the negative stiffness portion of the energy landscape of ferroelectric inclusions, constrained in a matrix, during thermally induced transformation to produce unique properties. Ultra-high dynamic stiffness and viscoelastic damping result from negative compressibility and shear modulus of the inclusions, respectively. Our metamaterials stabilize the negative stiffness region of one phase by its coherent integration with a stable phase at the nanoscale. Not only does this produce ultra-low stiffness due to the stabilization of a wide range of local lattice parameters, but this is accomplished under quasi-static, rather than dynamic, conditions.While demonstrated here for model NiAl alloys, the use of negative stiffness phases and landscape engineering to achieve mechanical properties not otherwise possible is quite general and applicable to any material exhibiting martensitic transformations, including ferroelectric44. Metals with stiffness in the low gigapascal range are highly desirable for integration with soft materials with similar stiffnesses. For example, the combination of ultra-low stiffness with metallic electronic conductivity and high strength furthers the possibilities for multifunctional, flexible electronics45 and would be desirable for implantable devices, enabling better integration with tissue47.We further note that free energy landscape engineering can be used for purposes other than achieving ultra-low stiffness; for example, the technique can be used to modify martensitic transition temperatures and thermal hysteresis48 with atomic visualization and CNA done using OVITO50. Within LAMMPS, a velocity-Verlet algorithm integrated with a 1 fs time step was used throughout. All simulations used a Nose–Hoover thermostat with a coupling constant of 0.01 ps; those with pressure control used a Nose–Hoover barostat with a coupling constant of 0.1 ps.All MD simulations were performed with the LAMMPS code30 was fit to experimental properties of Ni, Al, and Ni3Al from Voter et al.51, B2 NiAl52, as well as 3R L10 martensite and Ni5Al329. It is able to capture the main details of martensitic transformation in NiAl: the composition range for transformation and approximate transformation temperatures, in addition to the relative stability of the austenite and martensite. However, the potential does not predict exactly the complex R7 martensitic phase seen experimentally.The interatomic model from Farkas et al.63Al37 phase was then introduced by randomly switching Al atoms for Ni atoms until the 63% ratio was reached in the chosen region; see additional discussion in ref. 29.The off-stoichiometric structures were built by first generating equiatomic B2 NiAl systems of desired dimensions. The disordered Ni50Al50 and Ni63Al37 at 300 K with an NVT  ensemble. The systems were initialized as perfect B2 crystals of 512,000 atoms with 8 s−1. The third axis was left free to equilibrate during deformation with 1 atm pressure, as were all angles. The total free energy change for this isothermal process can be computed by 44. The metamaterial landscapes were produced by combining the pure NiAl and Ni63Al37 landscapes in varying fractions.The free energy landscapes were generated by straining samples of each single-phase Ni8 s−1) under an NVT ensemble. The stiffness for each composite fraction was obtained using multiple samples, each with unique randomizations for composition and atomic velocities. Samples were equilibrated for 100 ps, followed by two sets of alternating single period sine-wave strain and 50 ps equilibration. Five sine-wave strain cycles were then run, used for averaging and variability of the stiffness, taking linear fits every 0.25% in 1% increments. Each cycle was between 0 and 2% strain at a rate of 5 × 108 s−1. For fractions above 50 at% Ni63Al37, 10 samples were used due to significant variability from differing domain formation; at and below 50 at%, two samples were found to be sufficient to describe the uncertainty . The atomistic data from this study are available from the corresponding author upon reasonable request. Direct simulations for reproducibility are available through nanoHUB.org with the Nanomaterial Mechanics Explorer simulation tool53 (https://nanohub.org/tools/nanomatmech).Thermodynamic and selected structural data, as well as scripts for plotting and analyzing the data are available from the Strachan Research Group repository (Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Supplementary Movie 6


They adversely impact human and animal health and can be a scourge to agriculture, while in turn serving as a beneficial source for foods and beverages, new medications, and biocontrol. There are approximately 1.5 million different species of fungi on Earth, which largely reside in soil and plant. They are also readily found on human skin and within the gastrointestinal and genitourinary tract, yet only about 300 species are known to make people sick . Fungi aironment .Fungi also contribute significantly to managing human and animal diseases by producing major antiinfectives, immunosuppressant drugs, and anticholesterol statins. As they synthesize complex molecules using well-defined metabolic pathways, fungi are valuable for biotechnology. Their genetic manipulation and modulation of environmental growth conditions influence product formation. The production of soy, cheese, bread, wine, and beer have greatly benefited from modification of such factors, as they improve yield and food quality. Large-scale production and industrial application of fungal proteins makes higher fungi a valuable source of commercially significant proteins.Journal of Fungi (JoF) with this inaugural issue. On behalf of the editorial team, I would like to extend a very warm welcome to the readership of JoF. I want to take this opportunity to thank all of our authors, editors and dedicated reviewers, who will contribute to the success of the journal.There are many challenges to understand fungi in their natural environment, and the biological changes that occur when fungi adapt to new environments, especially within the human and animal hosts and in response to antifungal agents. We need to improve our methods to detect them in acute and chronic diseases, and we need to control and influence their growth in medical and agricultural settings. In this context, it is with great pleasure that we celebrate the launch of the JoF is an international, peer-reviewed scientific open access journal that provides an advanced forum for studies related to pathogenic fungi, fungal biology, and all other aspects of fungal research. It is our objective to advance fundamental knowledge of fungal biology, genetics and physiology, and address the growing importance of fungi in medicine, public health, health policy and security, agriculture, and the environment through innovative science and leading edge opinions.The The journal publishes reviews, regular research papers, and communications in quarterly issues. It is our aim to encourage scientists to publish their methodological details, experimental and theoretical results in as much detail as possible. Therefore, there is no restriction on paper length and associated supplemental materials.In addition to conventional contributions from the academic community, we encourage contributions from industry and public health professionals. Guest editors with novel theme concepts are always welcome and, of course, we encourage comments and suggestions that could improve the quality of the journal.JoF a valuable and influential voice to advance our understanding fungal sciences and the global importance of fungi.We hope you will find the Thank you!David S. Perlin


Concern has been raised about the similarities between the lower panel of The authors would like to apologize for any inconvenience caused to the readers of the journal.


This issue closes out Volume 2 of JACMP. I am pleased to note that during the year we introduced a couple of new features; Letters to the Editor and Book Reviews. Look for additional new features next year. We have had a 65% increase in the number of articles published over last year, and from the number of manuscripts currently under review we should see a similar increase next year.In addition to the Editorial Board, whose hard work and dedication I greatly appreciate, this journal would not be possible without the many hours voluntarily given by the referees. I wish to acknowledge and thank them for their contributions. Over 100 individuals have acted as referees in the last two years and their names are listed alphabetically at the end of this editorial.Important to the activities of the clinical medical physicist in the United States is the availability of certification by an appropriate governing body. After several years of careful thought and negotiation, two such bodies in the United States, the American Board of Radiology (ABR) and The American Board of Medical Physics, Inc. (ABMP), have reached a working agreement. This is of great importance to all clinical medical physicists in this country and I strongly recommend that they read the Letter to the Editor in this issue from the ABMP, which explains and contains this working agreement.Once again I am very pleased to announce that the JACMP will continue as a subscription‐free journal for 2002 allowing it to have worldwide accessibility. This would not be possible without our commercial sponsors and advertisers. They are listed on the journal home page. Please take a moment to thank them and let them know that you appreciate being able to access the JACMP without charge.Peter R. Almond, Ph.D.Editor‐in‐Chief


Postoperative visual acuity can be limited by post-keratoplasty astigmatism, even with a clear corneal graft. Astigmatism management can be performed by selective suture removal, adjustment of sutures, optical correction, photorefractive procedures, wedge resection, intra-ocular lens implantation, intracorneal ring segments, relaxing incisions with or without compression sutures and repeated keratoplasty. Relaxing incisions can be made in the graft, graft-host interface or host cornea. Despite the unpredictability of the method because the flat and steep meridians are usually not orthogonal after penetrating keratoplasty, with asymmetric power distribution, all the studies showed an overall reduction of refractive, keratometric or topographic astigmatism, ranging from 30% to 72% with manual or femtosecond-assisted techniques. Most patients with astigmatism higher than 6 diopters had residual cylinder less than or equal to 3 diopters, which can be treated by laser excimer ablation or secondary intraocular lens implantation. The aim of penetrating keratoplasty (PK) is to improve a patient’s postoperative vision. Despite a clear corneal graft, visual acuity can be limited by astigmatism . Based oPost-keratoplasty astigmatism management can be done before or after suture removal. If done before, it is called suture-in post-keratoplasty astigmatism. In this case, topography-guided suture manipulation, including selective suture removal  guided bAfter suture removal, post-keratoplasty astigmatism can be managed by optical correction such as contact lenses or glasses , which cThe purpose of this review is to provide surgeon updated information about post-keratoplasty astigmatism management with arcuate keratotomy and to help define a strategy for the correction of this post-keratoplasty astigmatism. Questions we intend to answer were: Is femtosecond laser-assisted relaxing incision better than conventional relaxing incision for the management of post-penetrating keratoplasty astigmatism? Which nomogram and which length and depth should we use for the correction?A review on PubMed and Cochrane was performed, analyzing all the publications from 1986 to 2017, concerning the topic of post-keratoplasty astigmatism management with keratotomies . Clinical trials, systematic review, non-randomized prospective study and case series of more than 6 patients, published in an English language journal with an impact factor greater than or equal to 2.0 were reviewed.The corneal graft-host junction generally heals by 1 year after transplantation and corneal surface stability is generally achieved by 3 to 4 months after complete suture removal . Before Factors influencing post-penetrating keratoplasty astigmatism involve the donor tissue and the host.Infant donor corneas induce greater astigmatism than adult donor tissues . Non-uniThe severity of the underlying disorder   also plaDuring surgery, trephination  ; graft sPost-operative drugs, inflammation, corneal vascularization, rejection and wound dehiscence affect wound healing and may be associated with astigmatic changes.Relaxing incisions can be made for patients with keratometric astigmatism, 3 to 4 months after complete suture removal . The maiThe site of arcuate keratotomy can either be in the donor cornea or at the graft-host interface, or in the host cornea . IncisioRelaxing incisions can be made manually  was introduced for the treatment of low astigmatism. These incisions are not opened anteriorly, decreasing the risk of epithelial disruption, infection or wound dehiscence. ISAK was successful for naturally occurring and post-cataract surgery astigmatism  and resiViswanathan & Kumar  treated Wetterstrand et al.  reportedLoriaut et al.  describeThe first step is ISAK made at a depth of 75% in eyes with penetrating keratoplasty. A LASIK flap of 100 μm was then made, lifted and ISAK were opened and the flap replaced. Laser excimer ablation can be made 1 month after for the correction of residual ametropia, after reopening of the flap.This technique was performed in 9 patients. The mean preoperative refractive cylinder was 6.11 ± 2.54 D, and the post-operative cylinder was 2.85 ± 1.31 D. No complications were reported. This technique affords advantages of ISAK with a greater astigmatism correction, but there are potential complications of the LASIK itself.Main results are summarized in Table Arcuate keratotomy can be useful as it creates minimal surface disruption, can treat nonorthogonal astigmatism, and yields rapid visual recovery. This technique ensures an optical zone of constant diameter and homogeneity of depth over the entire length of the incision. Typically, post-PK keratotomies are done to a depth of 75% to 80% corneal thickness, and incisions are made at an optical zone 1.0 mm central to the graft-host junction.Three different incision locations were described: graft, graft-host interface or host cornea. Refractive or keratometric pre-operative cylinder was between 11 D and 6.1 D, and an overall reduction ranging from 30 to 72% was observed, with manual or femtosecond-assisted techniques. Most of the patients with astigmatism higher than 6 D had residual cylinder less or equal to 3 D, which can be treated by laser excimer ablation, or secondary IOL implantation.In the reported studies, manual keratotomy seems to have more complications (in particular wound dehiscence) and poorer predictability than FSAK. However, all studies had a high variability in outcomes despite the use of a standardized method of surgery. The unpredictability seems to be correlated with many variables such as the value of the initial cylinder, individual variability, alignment.Individual variability responsible for limited predictability is related to the variability in the distribution of compressive forces and biomechanical constraints in the corneal graft. Therefore, arcuate keratotomy after PK necessitates its own nomogram, as the fibrotic rim created at the graft-host junction has a different tension than the natural limbus.Under-correction and inter-individual variability can be due to misalignment. This could be addressed by preoperative limbal marking but the direct visualization of the limbus can be masked by the suction ring during the procedure. Also, a control of cyclotorsion can be useful in this case. Femtosecond provides more depth accuracy than manual keratotomy as we have a pre-operative view of the cornea thickness thanks to the OCT.Unsuccessful correction and overcorrection can be explained by the pattern of irregular astigmatism , which leads to unpredictable correction. Some authors hypothesized that using topography-guided placement of relaxing incision can produce more predictable results, as shown by vector analysis using Alpin’s methods , 50, 51.Unpredictability can also be explained by the inherent dynamic instability of such incisions over time.It is possible that a smaller arc length would achieve the intended effect on astigmatism or increasing the angular length and reducing the optical zone diameter of the incisions as Wilkins et al.  reportedA refinement in the treatment nomogram for femtosecond laser-assisted AK for high astigmatism after PK remains a major issue. Further prospective studies with different length, depth and width correlated with the pre-operative cylinder and optical zone should be performed to define a new nomogram with a higher predictability.Manual procedures have some disadvantages: poor predictability, overcorrection, corneal perforation, wound dehiscence, and instability of corneal topography . In manuFSAK is safer and more predictable than manual procedures, but complications have also been described Table . OvercorNo standard surgical procedure for performing arcuate keratotomies for post-penetrating keratoplasties astigmatism exists, due to its unpredictability. However, all the studies show that patients with astigmatism higher than 6 D had residual cylinder less than or equal to 3 D, which can be further treated by laser excimer ablation or secondary IOL implantation.


Accurate prediction of graft survival after kidney transplant is limited by the complexity and heterogeneity of risk factors influencing allograft survival. In this study, we applied machine learning methods, in combination with survival statistics, to build new prediction models of graft survival that included immunological factors, as well as known recipient and donor variables. Graft survival was estimated from a retrospective analysis of the data from a multicenter cohort of 3,117 kidney transplant recipients. We evaluated the predictive power of ensemble learning algorithms  and compared outcomes to those of conventional models (decision tree and Cox regression). Using a conventional decision tree model, the 3-month serum creatinine level post-transplant  predicted a graft failure rate of 77.8% . Using a survival decision tree model increased the index of concordance to 0.80, with the episode of acute rejection during the first year post-transplant being associated with a 4.27-fold increase in the risk of graft failure. Our study revealed that early acute rejection in the first year is associated with a substantially increased risk of graft failure. Machine learning methods may provide versatile and feasible tools for forecasting graft survival. However, due to the many factors that influence graft survival, accurate prediction of transplant outcomes using standard statistical modelling is difficult4. New approaches, such as the use of data mining methods, could improve the precision and accuracy of predicting outcomes of organ transplant by taking into account numerous factors, as well as the complex interactions among these factors. Machine learning, which is the main technical basis for data mining, provides a methodology to extract information from the raw data in medical records6. Machine learning has been used to predict graft survival for kidney7, liver10, and heart-lung12 transplants. However, as factors that influence the outcomes can vary widely among different organ systems, there is discrepancy regarding the accuracy and effectiveness of prediction models based on machine learning for different graft types.Kidney transplant (KT) is well recognized as being the best treatment option for patients with end-stage renal disease13 delayed graft function (DGF)14, and allograft survival20. Over the past few decades, progress in immunosuppressive therapy, such as the use of cyclosporine, has definitely reduced the incidence rate of acute rejection of kidney grafts, as well as having improved short-term graft survival21. Therefore, interest has now shifted to forecasting the long-term survival of kidney allograft.In the field of KT, computational forecasting has been used in combination with clinical outcomes to predict chronic allograft rejection17, artificial neural networks18 and Bayesian belief networks20. Among these different attempts, a good correlation was identified between graft survival predicted using decision tree modelling and the observed 10-year survival rate calculated from survival data in the United States Renal Data System15: r-value of 0.98, with an area under the receiver operating curve (AUROC) of 0.90. In another modelling study, using the data from 1,542 KT recipients in the Australian and New Zealand Dialysis and Transplant Registry, the success or failure of a transplant was predicted with an 85% accuracy using artificial neural networks18. Bayesian net classifiers have been shown to predict the success or failure of the transplant with 97% accuracy. However, the accuracy for predicting longer-term graft survival duration was lower at 68%19. Unfortunately, current computational predictive models of long-term KT graft survival are limited by multiple factors, including: their sole reliance on pre-transplant factors, without consideration of immunological factors20; relatively small sample size used to build the models19; and failure to accurately use censored patient data20. Moreover, the observation time for existing models has been relatively short, while a sufficiently long observation period would be essential to predict long-term graft survival16. Due to insufficient research on survival duration of the transplanted kidney and knowledge of the determinants of long-term graft survival, combined with an inability to prioritize the large number of factors known to influence clinical outcomes after KT, current models have largely focused on predicting the success or failure of the transplant and not of its long-term survival19. Our aim in this study was to use representative data from a Korean population23 to compare data mining methods to standard statistical models to predict long-term graft survival after KT.Several attempts have been made to use machine learning tools for data mining to predict long-term graft survival. These attempts have included the use of decision tree-based modelling learningAmong the 3,117 KT recipients enrolled in our study, graft failure (GF) occurred in 304 (9.8%) over a mean follow-up period of 85.2 months. Baseline characteristics for patients who experienced GF, compared to those without GF, are summarized in Tables With regard to donor characteristics, 30.5% of patients in the GF group had received a deceased donor transplant, with 53.1% of patients in the non-GF group receiving a living donor transplant. No between-group differences were identified in terms of donor age, sex, kidney function at the time of donation, body mass index, and cytomegalovirus IgG titer Table .2 levels among patients who experienced GF than among those who did not  mismatch were the only differences identified between the two groups. Cyclosporine A was more frequently used as the maintenance immunosuppressant therapy among patients with GF, compared to those without GF. Moreover, patients with GF experienced more frequent episodes of infection during the first year post-KT. Laboratory findings at 3 months post-KT were significantly different between the two groups, with higher serum creatinine and lower serum sodium and total COWhen estimating the survival prediction at N years post-transplant, distortion can be introduced in the estimate when using data from people who have not been followed-up for N years. We tried to overcome the disadvantages of retrospective medical records, including the issue of missing data, with various methods, such as multiple imputation methods  as a being an important issue to consider. As shown in several retrospective studies using the same dataset, the proportion of DSA-positive patients in our study was very low. We assumed this low prevalence of DSA-positive patients (n = 23) was because these cases did not proceed to transplantation or that a positive DSA was not detected prior to the formal implementation of testing after 2008 Table . The dis25.Parameters used in our predictive model is summarized in Table The binary classification tree derived from the dataset by conventional methods is shown in Fig. Among various conventional decision tree (DT) models, the 3-month serum creatinine level post-transplant was identified at the first decision node, being the most important risk factor of GF  to a large dataset obtained from three centers in Korea, having expertise in KT. Second, we maximized the use of censored patient data, with survival statistics providing meaningful clinical information . Among patients who did not experience an episode of acute rejection within the first year after KT, the 3-month level of serum creatinine was the most important risk factor of graft loss  acute rejection is a cause of long-term graft failure33. Of note, the most recent data on GF post-transplantation identified an association between acute rejection in the first six months after KT and death-censored GF among 83,008 deceased donor kidney transplant recipients and 48,399 living donor kidney transplant recipients, adjusting for recipient characteristics and the kidney donor profile index (KDPI) for living donors33. Therefore, acute rejection in the first six months post-transplant substantially increases the risk of early and late graft loss33. Using a time varying Cox regression analysis, Koo et al. reported that both early acute rejection and late acute rejection were related to GF in a Korean population30. These results are consistent with our findings.Although the association between biopsy-proven acute rejection (BPAR) and early graft loss is well-known35. The persisting debate on the effects of early acute rejection on the long-term graft survival may be due to differences in how the rejection episode is defined between studies, as well as differences in the characteristics of the study population. As an example, previous studies that have reported a negative association between early acute rejection and graft loss limited their analysis to a rejection event sustained within the first 3 months post-transplant35. In addition, the initial diagnosis of graft rejection was made clinically without confirmation by biopsy35. Studies conducted in Korea have defined early acute rejection as an event that occurs up to 1-year post-transplant30.Many studies have investigated the relationship between graft survival and the timing of acute rejection, and it seems clear that late transplant rejection has a deleterious effect on the survival rate of organs39. Recently, using data from the Australia and New Zealand Dialysis and Transplant Registry, Clayton et al. identified a 30% decrease in the glomerular filtration rate (GFR) at 1-year post-transplant, from baseline obtained immediately after KT, to be the best predictor of graft survival, in combination with a 2-fold increase in serum creatinine and absolute glomerular filtration rate40. Our study does support these findings, with early graft rejection and the 3-month serum creatinine level being predictive of long-term graft survival.Among patients who did not develop transplant rejection within 1 year in our study group, the level of serum creatinine at 3 months post-transplant was the most important predictor of graft loss, with age of the recipient and length of observation from the time of transplant being additional risk factors Fig. . The pret’, from a certain time point of observation, being predictable for observations in Fig. 26. To overcome these problems, classification can proceed using censored date, with survival statistics applied to various machine learning techniques with different attribute settings 15. However, the researchers did not identify the factors associated with graft loss. Focusing on recipients of grafts from living donor from a single center having KT experience in Egypt, Fouad et al. also compared decision tree classifiers to regression analysis17. However, their comparison was limited by insufficient information regarding their model attributes, inclusion of pre-transplant information only and a relatively small study group. In our study, we also tried conventional decision tree modelling to predict the 10-year graft survival with weighting method  data, even though we applied different model settings to address this issue . Shabir et al. provided a more detailed method for predicting outcomes, with external validation against data from a multicenter cohort49. This study showed good-to-excellent discrimination for death-censored transplant failure  using data at 12-months post-transplant to develop their model. The absence of external validation is a major limitation of our study.The limitations of our study need to be acknowledged. First, donor and recipients ethnicity was not considered in our models. Previous studies from western countries have included Asians living in western countries. It is likely that environmental differences will influence clinical outcomes for patients of the same ethnicity living in different countries. Moreover, the characteristics of Asian individuals are likely to vary even between middle and eastern regions of Asia . Overall, the inclusion of Asian populations has been quite limited in studies of kidney transplantation. In fact, in the landmark trial for transplant outcomes by Wolfe sue Fig. . Moreno-Despite these limitations, our model survival decision tree model improved the accuracy of predicting GF  over conventional decision tree and Cox regression models. As such, our model might be clinically relevant to identify patients with a specific group of risk factors associated with graft loss and, thus, enable clinicians to predict outcomes of KT. Moreover, we do provide evidence supporting the application of advanced machine learning techniques, using survival statistics, in the field of KT. Using this approach, we have prioritized risk factors of graft failure, with acute rejection, confirmed by renal biopsy, within 1 year of KT being the most important risk for graft failure. We also emphasized the importance of post-transplant serum creatinine level as a predictive factor. Our findings will assist clinicians in predicting outcomes of KT.Our study cohort included 3,117 adults (>18-years-old) who had undergone KT at three tertiary care hospitals with expertise in KT in Korea, between 1997 and 2012. Our analysis included >50 attributes extracted from 3,117 medical records of recipients and donors, which included complete records for 2,017 recipients. The institutional review board at each hospital approved our methods  and the study was performed according to the ethical standards of the Helsinki Declaration. This study is a retrospective, non-interventional study, and written informed consent was waived due to the study’s design.versus tacrolimus); and a history of pre-transplant ischemic heart disease, peripheral vascular disease, diabetes mellitus, hypertension, and hepatitis B or C virus infection. Duration of observation from the endpoint of follow-up (23 March 2015) was included as a attribute, measured from the time of transplant to the endpoint of follow-up, which reflects the transplant era.The following recipient-related factors were extracted from the medical records for analysis: age; sex; smoking, primary cause of end-stage renal disease (ESRD), body mass index (BMI), ABO type and ABO incompatible transplant; infection history within 1 year after transplant, type of maintenance immunosuppressant  A, B and DR, and cross matching results, were reviewed.The following pre-transplant laboratory levels were also obtained for analysis: serum creatinine, hemoglobin, white blood cells (WBC), albumin, cholesterol, low-density lipoprotein (LDL), and high-sensitivity C-reactive protein. The following pre-transplant immunological factors were also included in the analysis: donor specific antibody and panel reactive antibody titer. The following laboratory findings obtained at 3 and 6 months post-transplantation were also included in the analysis: serum creatinine; serum Na; total COAmong all factors considered, 33 independent attributes likely to influence graft survival were selected for inclusion in our prediction models, using individual and ensemble learner with multiple imputation Tables  and 2.25 and ensemble learning models (Bagging and Random Forest)43 were implemented.We created training sets for machine learning using 80% of the overall dataset. We ensured a 10-fold cross validation for our training process, with performance of the trained model evaluated using 20% of the overall dataset. We used five different seeds to measure the final performance, with the index of concordance considered to be the main criteria to evaluate model performance. Two widely used individual learning models 24. R statistical language  and the MICE package were used for imputing missing values for continuous and categorical data .Our study included data obtained over an 18-year period, from 1997 to 2012. To overcome the problem of differences in transplant technique across this time period, including differences in variables evaluated pre-transplantation and monitored after transplant, we evaluated multiple data imputation methods, defined in statistical libraries in R, selecting the multivariate imputation by chained equation (MICE) method, with supplemented attributes imputation of missing values25. As decision trees are more expressive then other models in terms of their classification process, they are easier to implement and interpret than many other machine learning algorithms, as well as being applicable to non-linear datasets. CART formulation forms a binary tree and minimizes the training error in each leaf. CART uses a Gini coefficient to choose the best variable, which estimates the purity of the internal nodes. Tree models represent data by a set of binary decision rules25. Logistic regression is based on the logistic function with a linear combination of dependent variables, and is formulated as: π(x) = 1/1 + e − (β X) where βt X = β0 + β1 × 1 + β2 × 2 + …, with π(x) as the probability p(y = 1|X) that the dependent variable (y) is of class 1, given the independent variables(xi)25. We also used the generalized linear model (GLM) library in R to fit the logistic regression model41. The GLM for baseline modelling used a binomial distribution for the response with a logit link function41.Decision tree, also called Classification and Regression Trees (CART), is a conceptually simple approach but one that is statistically powerful42, which builds predictive models by using repeated bootstrap samples from training dataset and aggregates those predictors. For aggregation, the average is used for regression model and plurality vote for classification model42. We chose CART as a base learner among various algorithms.Ensemble methods classify data by combining the results of multiple learners, with the aim of improving the predictive performance of a given statistical learning model or a fitting technique. Bagging and random forest are different ensembling techniques. Bagging is the acronym of bootstrap aggregating43.The random forest algorithm adds more randomness to bagging. However, using a decision tree as base learner, the random forest algorithm randomly chooses a specific number of attributes at each node and finds the best split among these attributes26.Conventional prediction models are inherently limited by censored data used with individual and ensemble learners Fig. . Typicalc_i: the observed event count for observation i, (2) t_i: the observation time for observation i, (3) Observed event rate: A general decision tree was built using a process of recursively finding a split rule, such as the Gini index or entropy index, which lowered the impurity in classified data of patients’ outcome. Survival decision tree uses survival statistics as the split rule criteria, which is formulated as follows: (1) 26.The goal is to maximize the improvement for each split, which is equivalent to minimizing the within-node deviation. Based on this process, we can identify the tree model that maximizes the likelihood of the a specific classification at a specific node and, thus, optimizes the fit of the data


Stu2/XMAP215 microtubule polymerases use multiple tubulin-binding TOG domains and a lattice-binding basic region to processively promote faster elongation. How the domain composition and organization of these proteins dictate polymerase activity, end localization, and processivity is unknown. We show that polymerase activity does not require different kinds of TOGs, nor are there strict requirements for how the TOGs are linked. We identify an unexpected antagonism between the tubulin-binding TOGs and the lattice-binding basic region: lattice binding by the basic region is weak when at least two TOGs engage tubulins, strong when TOGs are empty. End-localization of Stu2 requires unpolymerized tubulin, at least two TOGs, and polymerase competence. We propose a ‘ratcheting’ model for processivity: transfer of tubulin from TOGs to the lattice activates the basic region, retaining the polymerase at the end for subsequent rounds of tubulin binding and incorporation. These results clarify design principles of the polymerase. S. cerevisiae) or Alp14 and Dis1 (from S. pombe) are homodimers, whereas orthologs in higher eukaryotes  are monomeric (Microtubules are dynamic polymers of αβ-tubulin (hereafter: tubulin) that have critical roles in chromosome segregation and intracellular organization  were functional polymerases with maximal activity about half that of wild-type Stu2. We identified an unexpected, antagonistic relationship between the TOGs and the basic region: Stu2 coats the microtubule lattice without detectable end preference when its TOGs are ‘empty’, but this lattice binding is attenuated and plus-end specificity is restored when at least two TOGs engage unpolymerized tubulins. Tubulin-induced antagonism of lattice binding suggests a ratcheting model that may explain processivity: delivery/release of polymerase-bound tubulins to the microtubule end ‘activates’ the basic region, which helps maintain the now empty polymerase at the growing end, poised to capture more unpolymerized tubulin for another round of incorporation. Both polymerase activity and tubulin-induced attenuation of basic:lattice interactions were diminished by perturbations that make tubulin:tubulin interactions stronger. These perturbations apparently lead to futile cycling of the polymerase by bypassing the normal requirement for a microtubule end to stimulate release of TOG-bound tubulins. Our findings provide new insights into how the design of the polymerase determines its activity, and reveal that the tubulin conformational cycle plays a more central role in modulating the activity of this family of polymerases than previously appreciated.Stu2 forms a homodimer, with each monomer containing (from N to C) a TOG1 domain, a TOG2 domain, a basic region, and a coiled-coil that mediates dimerization . In an eTo determine whether polymerase activity depends on the number or identity of TOG domains (TOG1 versus TOG2), we measured the polymerase activity of Stu2 variants containing R->A mutations  that inactivate the individual TOG domains for tubulin binding , 2012. SWhile these dimeric Stu2 variants with TOG1 or TOG2 inactivated displayed modest decreases in activity but normal end-binding affinity, a monomeric Stu2 variant  behaved differently. Stu2-∆cc displayed a substantial decrease in activity as well as greatly reduced end-binding affinity compared to dimeric variants . Why didThe experiments described in this section demonstrate that the polymerase activity of Stu2: (i) does not strictly require different kinds of TOG, (ii) is approximately proportional to the number of tubulin-binding TOG domains (when positioned as in wild-type), and (iii) is modulated by the ‘strength’ of the basic region . The obsBased on the similarity of their structural and biochemical interactions with tubulin, we argued previously that TOG1 and TOG2 were likely to be interchangeable in the polymerase mechanism . Here, wWe first measured fluorescence intensity as a function of concentration for the wild-type Stu2-eGFP spot on the microtubule end, using the same TIRF assay . The intidentity of the active TOG, the position of the active TOG in the primary sequence, or both. Stu2(TOG2-TOG2*)  accumulated to comparable levels as TOG1-TOG2*  and Stu2(TOG1-TOG2*) variants accumulated to different extents on the microtubule end. Whereas Stu2(TOG1*-TOG2) was present in amounts comparable to wild-type, Stu2(TOG1-TOG2*) only reached about half that level . These dG1-TOG2* . Thus, iG1-TOG2* , despiteG1-TOG2* .Why does inactivating TOG2 for tubulin-binding lead to an increase in specific activity? We speculated that a region of TOG2 outside the conserved tubulin-binding interface might be required for plus-end recognition/localization, and that tubulin binding to TOG2 might antagonize this role. If true, then deleting TOG2 should yield different and stronger effects than mutating TOG2. We therefore prepared Stu2 variants in which the basic-proximal TOG2 domain was deleted (e.g. Stu2(TOG1-∆TOG2)). Control experiments demonstrated that deleting TOG2 did not compromise the ability of TOG1 to bind curved tubulin, or of the basic region to bind the lattice . HoweverA genetic rescue assay provides additional support for the special nature of the basic-proximal TOG2 domain: deleting TOG2 compromised rescue most severely whereas mutating TOG2, or deleting or mutating TOG1 domain, affected rescue less severely (and comparably to each other) . Stu2 fuThe mechanisms of processivity in Stu2/XMAP215 family polymerases are not well understood. We wondered if differences in processivity might explain why Stu2(TOG2-TOG2*) and Stu2(TOG1*-TOG2) differ more than 2-fold in specific activity, despite both using TOG2 as their only tubulin-binding TOG. The hyperbolic fit to the fluorescence intensity of Stu2 on the microtubule end indicates that when the concentration of Stu2 is 5 nM  or Stu2(TOG1-TOG2*). Inactivating the TOG1 domain yielded a modest decrease in dwell times (1.7 s for Stu2(TOG1*-TOG2) compared to 2.2 s for wild-type) . In contCurrent models for XMAP215/Stu2 family polymerases assume that lattice binding by the basic domain is independent of tubulin binding by the TOGs (e.g ). HoweveDoes tubulin-induced attenuation of lattice binding require that all four TOGs engage with tubulin? Dimeric polymerases with TOG1 or TOG2 inactivated (Stu2(TOG1*-TOG2) or Stu2(TOG1-TOG2*)) showed robust tip localization with little binding to the bulk lattice . The monWe next tested a panel of tubulin mutants  to definWe first speculated that the negatively charged C-terminii of TOG-bound tubulins might themselves bind to the basic region, effectively competing with the microtubule lattice for the basic region. To test this idea, we purified ‘tail-less’ tubulin lacking the C-terminii of α- and β-tubulin. This tail-less tubulin also inhibited Stu2 binding to the lattice  or lateral (side-to-side) tubulin:tubulin interfaces. Gel filtration  and analC. elegans  between those TOG-bound tubulins substantially reduce lattice-binding affinity . We wond elegans , to begi elegans . Thus, iMicrotubule end recognition and binding to unpolymerized tubulin are mediated by preferential binding of polymerase TOG domains to the curved conformation of tubulin , 2012. HTo our surprise, Stu2 barely stimulated elongation and showed weak to no plus-end specificity on dynamic β:T238A microtubules . InsteadWe had expected based on prior XMAP215/taxol experiments  that StuStu2 variants with TOG1 or TOG2 as the only active TOGs (i.e. competent to bind tubulin) were each functional polymerases. Thus, the quantitative measurements of polymerase activity reported here show that while polymerase activity requires at least two TOG domains, the two TOGs need not be different types. Monomeric Stu2 variants with a ‘dimer equivalent’ basic domain stimulate elongation to a comparable extent as homodimeric variants with an equal number of tubulin-binding TOG domains, so polymerase activity also does not depend strongly on oligomerization state. That monomeric and dimeric Stu2 variants can have comparable activity indicates that polymerase activity is largely insensitive to the way that the TOGs are linked. Indeed, in the monomeric Stu2-∆cc-2xBasic variant TOG1 is linked to TOG2 by the natural ~70 amino acid linker. However, in the dimeric Stu2(TOG1-TOG2*) variant the two active TOG1s are linked by two much longer segments that encompass the natural linker, the mutated TOG2 domain, and the ~100 amino acid basic region that connects to the coiled-coil. These new findings are broadly consistent with the tethering-based mechanism we proposed previously .We made multiple observations that were not predictable from structural and biochemical properties of isolated TOGs. These include (i) the ‘positional’ separation of function between the N-terminal and basic-proximal TOG domains in Stu2, (ii) the antagonism between TOG:tubulin and polymerase:lattice binding, and (iii) the loss of polymerase activity that accompanies perturbations that strengthen tubulin:tubulin interactions. These findings provide new insights into the molecular logic and functional design of these polymerases, and their implications for mechanism will be discussed in subsequent sections.Stu2(TOG1*-TOG2) and Stu2(TOG1-TOG2*) were present on the microtubule end in different amounts , showed Deletion of the TOG2 domain eliminated plus-end selective localization  in additA synthetic polymerase can be constructed from TOG1-TOG2 and an unrelated basic element . For thiStu2 and XMAP215 can both diffuse on the microtubule lattice in a way that depends on their basic regions and on the negatively charged C-terminal tails of α- and β-tubulin , but ourSurprisingly, mutation- or drug-induced perturbations that promote tubulin self-association and ‘straightening’  led to aThe ability of a single polymerase to bind multiple tubulins results in a high local concentration of tubulin. How might perturbations that favor tubulin self-association actually reduce polymerase activity? Under normal circumstances (wild-type tubulin without Epothilone present), even the high local concentration of TOG-bound tubulins is presumably not sufficient to overcome the energetic cost of tubulin straightening. This barrier to straightening must help prevent premature tubulin release from TOGs away from the microtubule end. Consequently, the preference of tubulin to be curved antagonizes polymerase binding along the body of the microtubule. By contrast, in the presence of mutation- or drug-induced perturbations that strengthen tubulin self-association and straightening, Stu2 behaves as if it were empty: it binds the lattice without detectable end preference. Thus, diminishing the energetic cost of self-association-induced tubulin straightening must lead to nonproductive release of polymerase-bound tubulins away from the microtubule end. That this nonproductive release also eliminates preferential end binding by Stu2 reveals an unanticipated link between polymerase mechanism and the tubulin conformation cycle. If tubulin straightens too easily, polymerase activity is lost to futile cycling, in which straightening-induced release from TOGs is no longer restricted to the microtubule end.Stu2/XMAP215 family polymerases have been compared to formins , which aStrep-tag II, were a gift from Dr. Gary Brouhard: Stu2 residues 1–658 ; Stu2 residues 1–761 ; Stu2 residues 1–888 . Mutations were introduced into the monomeric (658) or fully dimeric (888) background using QuikChange (Stratagene) mutagenesis or traditional restriction-based cloning after PCR. The TOG1-TOG1 Stu2FL plasmid was made using NEBuilder HiFi DNA Assembly . A gene block (gblock) encompassing TOG1 (residues 1–272) and the Stu2 basic domain (560-661) was purchased from IDT DNA . The ‘transplanted’ region encompasses the structured TOG1 domain with a C-terminal extension of ~20 additional amino acids from the linker sequence; shorter constructs that did not include this linker sequence proved to be unstable upon purification. The parent plasmid, excluding the region coding for the to-be-replaced TOG2 sequence, was amplified in a second reaction with primers sharing overlap to the gene block. The two products were mixed, incubated and transformed according to the manufacturer’s instructions. The integrity of all expression constructs was confirmed by DNA sequencing.Plasmids to express wild-type and β:T238A yeast αβ-tubulin were previously described . PlasmidS. cerevisiae using Ni-affinity and ion exchange chromatography, as previously described  containing 50 µM GTP or storage buffer for analytical ultracentrifugation experiments  containing 20 µM GTP. Expression of all Stu2p constructs, wild-type and mutant, were induced in E. coli using Arctic Express Cells with N-terminal His6 tags and C-terminal eGFP and Strep-tag II. Samples were induced with 0.5 mM IPTG for 24 hr at 10°C. Cell pellets were resuspended in lysis buffer  and sonicated for 30 min in the presence of PMSF before clarification by centrifugation. Cleared lysate was loaded onto a His60 Superflow Column (Clontech) and the final sample was eluted in lysis buffer containing 300 mM imidazole. Pooled elution fractions containing Stu2 were loaded onto a 3 mL Strep-Tactin Superflow column  and eluted in RB100  containing 5 mM desthiobiotin. For storage, final samples were exchanged into RB100 with 2 mL, 7K MWCO Zeba spin desalting columns (Thermo Scientific). Expression of purification of TOG1 domain from Stu2  was previously described  yeast αβ-tubulin were purified from inducibly overexpressing strains of escribed . Tubulinescribed , 2014.2, 1 mM EGTA), followed by 10 min incubation with sea urchin axonemes. Chambers were then blocked with 1% F-127 Pluronic in BRB80 for 5 min, and washed with 1X PEM  containing 1 mM GTP. Reaction chambers were sealed with VALAP after addition of Stu2p and αβ-tubulin samples.Flow chambers were prepared as described previously , with thSamples of Stu2p wild-type or mutant proteins, along with wild-type or β:T238A yeast αβ-tubulin were prepared in imaging buffer   A subsetMicrotubule (MT) dynamics and Stu2 location/signal were imaged by total internal reflection fluorescence (TIRF) microscopy using an Olympus IX81 microscope with a TIRF ApoN 60x/1.49 objective lens, a 491 nm 50 mW solid-state laser and Hamamatsu ORCA-Flash2.8 CMOS camera (Olympus). In assays where Stu2 signal was not detectable on growing MTs or no Stu2 was present in the reaction, MT dynamics were imaged by differential interference contrast microscopy (DIC) using an Olympus IX81 microscope with a TIRF ApoN 60x/1.49 objective lens and DIC prisms. Illumination at 550 nm was obtained by inserting a bandpass filter of 550/100 nm (Olympus) in the light path. Temperature for all assays were maintained at 30˚C using a WeatherStation temperature controller with enclosure fit to the microscope’s body. Micro-Manager 1.4.16  was usedIn TIRF assays, MT dynamics were recorded by taking an image every 3–5 s for 15–30 min. MT growth rates were measured manually by creating kymographs using the ReSlice plugin for ImageJ . From thFlow chambers were prepared as described above. Samples of Stu2p (wild-type or mutant) with wild-type yeast αβ-tubulin were prepared in imaging buffer. For all single molecule end-intensity experiments, Stu2 concentration was held at 5 nM and wild-type yeast αβ-tubulin was used at 1–1.5 µM. For measuring end intensity as a function of Stu2 concentration, concentrations of Stu2 between 10 and 300 nM were used; wild-type yeast tubulin concentration was held constant at 800 nM.MT dynamics and Stu2 location were imaged at 30˚C by TIRF microscopy as described above but using an Andor EMCCD iXon (Andor) camera. Images were recorded every 3 s over a period of 10–15 min.End intensity measurements to determine the number of Stu2 molecules at the end of a MT were all manually measured in ImageJ . In singFlow chambers were prepared and imaged as described above. For ‘strict’ single molecule processivity experiments, Stu2 concentration was held at 5 nM and wild-type yeast αβ-tubulin was used at 1–1.5 µM. For ‘spike’ single molecule processivity experiments at higher overall Stu2 concentration, 5 nM Stu2-eGFP was added along with 195 nM unlabeled Stu2-KCK, with 800 nM wild-type yeast αβ-tubulin.MT dynamics and Stu2 location and processivity were imaged by TIRF microscopy at 30˚C using an Andor EMCCD iXon (Andor) as described above. Images were recorded using streaming acquisition with a 100 ms exposure for 30–60 s.End processivity times of Stu2 proteins were measured manually, by creating kymographs using the ReSlice plugin for ImageJ . The lenTo prepare stable wild-type yeast MT, wild-type yeast αβ-tubulin was polymerized in the presence of 500 µM GTPγS in assembly buffer (see above). The mixture was incubated for 90 min at 30°C.Flow chambers were prepared as described above. Stable microtubules were attached to the cover slip using His-Tag Antibody , which was incubated in the chamber for 10 min before blocking with 1% Pluronic F-127 in BRB80 for 5 min, followed by a wash with BRB80. Pre-formed, GTPγS-stabilized wild-type yeast MTs were incubated in the chamber for 10 min, followed by a wash with BRB80 to remove unbound MTs. Solutions containing a range of Stu2 concentrations, with or without free wild-type yeast αβ-tubulin in imaging buffer were mixed, incubated on ice for 10 min, then introduced into the chamber immediately prior to data collection. MT fields were imaged at 30°C by TIRF microscopy as described above using an Olympus IX81 microscope with a Hamamatsu ORCA-Flash2.8 CMOS camera (Olympus), to view the presence and location of Stu2-eGFP molecules; fields were also imaged by DIC microscopy as described above. Images of MTs under TIRF and DIC fields were taken every 30 s for about 5 min. MT images were viewed using ImageJ .Depolymerization experiments using this assay setup were performed as previously described , using tGel filtration binding experiments were performed by loading 500 μL samples of TOG, tubulin wild-type or mutants (α:H284A or β:F281A) or mixtures of TOG:tubulin samples onto a Shodex KW-803 column equilibrated in BRB80  with 50 μM GTP. Samples tested contained either 1 μM TOG1-tail (1-317), 1 μM tubulin wild-type or mutant alone (α:H284A or β:F281A), and 1 μM TOG1-tail with 1 μM tubulin wild-type or mutant (α:H284A or β:F281A). All samples were prepared, allowed to equilibrate on ice for 20 min and then loaded onto the column.2, 1 mM EGTA, 100 mM NaCl) containing 20 µM GTP with a 2 mL, 7K MWCO Zeba spin desalting columns (Thermo Scientific). The samples shown in c(s) methodology in SEDFIT  were dialyzed into final buffer conditions of RB100  to form MTs, experiments were performed as previously described . BrieflySaccharomyces cerevisiae strains used in this study are described in stu2-3HA-IAA7 and a LEU2 integrating plasmid containing wild-type pSTU2-STU2-3V5 (pSB2232) are described in (STU2 variants were constructed by mutagenizing pSB2232 as described in (pSTU2-STU2(∆1–281)−3V5, i.e. TOG1∆), pSB2257 (pSTU2-STU2(∆282–550::GDGAGL)−3V5, i.e. TOG2∆), pSB2306 (pSTU2-STU2(R200A)−3V5), pSB2307 (pSTU2-STU2(R519A)−3V5), pSB2620 (pSTU2-STU2(∆12–245::321–559)−3V5, i.e. TOG2-TOG2), pSB2817 (pSTU2-STU2(∆326–550::1–272)−3V5, i.e. TOG1-TOG1).ribed in . STU2 varibed in , resulti600 ~1.0 from which a serial 1:5 dilution series was made and spotted on YPD + DMSO or YPD + 500 μM IAA (indole-3-acetic acid dissolved in DMSO) plates. Plates were incubated at 23°C for 3 days.For the spotting assay, the desired strains were grown overnight in yeast extract peptone plus 2% glucose (YPD) medium. The following day, cells were diluted to OD 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 favorably evaluated by Andrea Musacchio (Senior Editor) and three reviewers, one of whom is a member of our Board of Reviewing Editors. The reviewers have opted to remain anonymous. As you will see below, the three reviewers are keen on publishing your paper, as long as you can make the suggested changes.Thank you for submitting your article "Design principles of a microtubule polymerase" 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:S. cerevisiae Stu2 contribute to polymerase activity. The start off by showing that activity is basically proportional to number of active TOGs, in agreement with shown for Widlund et al. for XMAP. They make the unexpected observation that TOG identity plays a much smaller role compared to TOG position with respect to the basic microtubule lattice binding region(s). Furthermore, in a series of detailed experiments combining TOG and tubulin point mutants, they find that TOG-tubulin interaction appears to modulate lattice binding. Finally, using a tubulin mutant and epothilone B to stabilize or "straighten" the microtubule lattice, they show that Stu2 lattice binding is affected by tubulin curvature. Based on this data, they present a model where strong TOG:tubuin interaction is accompanied by weak lattice binding before tubulin incorporation. After release of tubulin into the growing microtubule end, lattice binding affinity increases. This results in the observed plus end specific localization of Stu2 which appears to require an active polymerase. As a whole, this work presents compelling data on the mechanism of action of an important family of microtubule regulators. The experiments are well designed and generally support the conclusions drawn.The Stu2/XMAP215/chTOG family of proteins are key regulators of microtubule dynamics. They are potent catalysts of microtubule growth and achieve this through a combination of conserved, N-terminal, tubulin-binding TOG domains and additional domain(s) that can bind to the microtubule lattice. In "Design principles of a microtubule polymerase," Geyer et al. carefully examine how the different domains of Essential revisions:You use point mutations in the TOG domains that argue that the number and type of TOGs is not important. However, to make the general claim that TOG is not particularly important, TOG2 would also need to be replaced with TOG1 in an identical experiment. Is there a reason this was not done? You do use a spot test in Figure 2—figure supplement 1C to show some activity of a TOG1-TOG1 construct in vivo. However, very similar rescue is seen with a TOG1-dTOG2 construct which was shown to be inactive in vitro . Therefore, since the ability of these constructs to rescue in vivo do not appear to directly reflect polymerase activity in vitro, polymerase activity of TOG1-TOG1 construct (or any construct with TOG1 adjacent to the lattice binding domain) will need to be demonstrated. Otherwise, the claim as stated in the Abstract, "polymerase activity does not require different kinds of TOGs, nor are there strict requirements for how the TOGs are linked" will need to be modified.We would also like you to clarify the affinity of TOG domains for mutant tubulins. What are the affinities of TOG domains for the tubulins with mutations ? Is it possible that differences in affinity cause the observed effects on Stu2-microtubule binding ?The way it is cited, the authors appear to suggest (subsection “Unpolymerized tubulin controls lattice binding by the basic region” –) that Brouhard et al., 2008 present evidence in support of a "tubulin shuttling" model. However, you present the contrary: XMAP215 does not act as a shuttle but as a tip tracking processive polymerase. In their model, XMAP215 (and bound tubulin) flux to the MT end was not fast enough to contribute to polymerization activity. If this was not the intention, please clarify.Other points to consider:A further experiment that was suggested was to investigate the distance between TOG domains and the basic region. It is unclear if the length/sequence of the linker connecting the TOG domain to the basic region is important for the activity. As this is a key point additional experiments are needed. Could the linker length be varied? Aligning sequences across different organisms may be helpful in the design of such constructs. Including these alignments in a supplementary table may also useful to readers. However although this would strengthen the paper, we see this as potentially beyond the scope of this paper, and leave this to your discretion.The Reviewing Editor was also concerned about the extent to which Stu2 is processive and whether their experiments to measure the end-residence time of Stu2 are the right ones.With 5 nM Stu2, you measured an end-residence time of ~2 seconds for a microtubule that is growing at ~0.5 μm/min. That growth rate converts to ~13 dimers per second per end, or 1 dimer per protofilament per second. This means that 2 second residence time isn't much for a protein with 4 TOG domains in the dimer. Although you point out that XMAP215 stayed on the end for ~4 seconds, the growth rate in those XMAP measurements was 3 μm/min or 82 dimers/sec/end. In other words, one could argue that Stu2 is not processive. Within those 2 seconds, it deposits its 4 tubulins and leaves.But you are measuring the end residence time at 5 nM Stu2, a concentration which is basically the same as the "uncatalyzed rate". It might look different if you measured the residence time at 100 or 200 nM Stu2, when the microtubule is growing 5 times faster. Either the end-residence time will be much shorter (~0.4 seconds) or the processivity will be much higher. Those experiments are challenging because you need a small amount of tagged Stu2 alongside a higher concentration of untagged Stu2, or maybe you need to do FRAP and measure recovery rates.This point is important for how you interpret the high affinity of Stu2 for microtubules after tubulin release. You argue the high affinity is a ratcheting mechanism that explains processivity. That can't be the main interpretation if there is no processivity.The amount of Stu2 at the microtubule end seems to saturate before the growth rate effect saturates. More specifically, the Stu2 signal at the end hits max at 20 nM while the growth rate saturates at 60 nM. The authors describe these concentrations as equivalent, and I get that, but our working concentration range is 0-->200 nM effectively, so in fact it's a large difference. we wonder if this indicates that the tip structure of the microtubule end determines how effectively Stu2 can do its work. This might explain why the processivity numbers are so low – maybe Stu2 is not effective on a blunter microtubule end but works most effectively on an end that is tapered?Please also discuss a model for how the proposed 'antagonism' between the basic region and the TOG domains could work. Essential revisions:You use point mutations in the TOG domains that argue that the number and type of TOGs is not important. However, to make the general claim that TOG is not particularly important, TOG2 would also need to be replaced with TOG1 in an identical experiment. Is there a reason this was not done? You do use a spot test in Figure 2—figure supplement 1C to show some activity of a TOG1-TOG1 construct in vivo. However, very similar rescue is seen with a TOG1-dTOG2 construct which was shown to be inactive in vitro . Therefore, since the ability of these constructs to rescue in vivo do not appear to directly reflect polymerase activity in vitro, polymerase activity of TOG1-TOG1 construct (or any construct with TOG1 adjacent to the lattice binding domain) will need to be demonstrated. Otherwise, the claim as stated in the Abstract, "polymerase activity does not require different kinds of TOGs, nor are there strict requirements for how the TOGs are linked" will need to be modified.The reason we had not measured the activity of a Stu2 variant with a TOG1 domain in the basic-proximal position is that ‘transplanting’ a TOG1 domain turned out to be more sensitive to the domain boundaries than we had expected (or observed for TOG2). We have now succeeded in purifying a ‘TOG1-TOG1’ variant of Stu2, and we show that it has polymerase and end-tracking activity . The ‘trick’ to achieve this was to include some of the linker sequence (~250-272) that follows the structured core of the TOG1 domain (roughly amino acids 10-250). The TOG1-TOG1 polymerase was less active than a TOG2-TOG2 polymerase, perhaps reflecting the additional sequence between the end of the transplanted TOG1 domain and the beginning of the basic region (see the response to the first ‘other points to consider’ for more about this). In any event, these new data clearly demonstrate that a Stu2 variant that only contains TOG1 domains can exhibit polymerase activity. Thanks for the push to get these data.We would also like you to clarify the affinity of TOG domains for mutant tubulins. What are the affinities of TOG domains for the tubulins with mutations ? Is it possible that differences in affinity cause the observed effects on Stu2-microtubule binding ?Excellent question. We now include quantitative binding experiments demonstrating that the TOG1 domain from Stu2 binds with comparable affinity to wild-type, ‘top-blocked’, and ‘side-blocked’ αβ-tubulin . More definitively than the gel filtration data we showed initially, these new experiments rule out mutation-induced changes in TOG-binding-affinity as a possible explanation for the effects we report in Figure 5. Chad Brautigam helped us collect and analyze these data, and consequently he has been added as an author to the revised paper.The way it is cited, the authors appear to suggest (subsection “Unpolymerized tubulin controls lattice binding by the basic region”) that Brouhard et al., 2008 present evidence in support of a "tubulin shuttling" model. However, you present the contrary: XMAP215 does not act as a shuttle but as a tip tracking processive polymerase. In their model, XMAP215 (and bound tubulin) flux to the MT end was not fast enough to contribute to polymerization activity. If this was not the intention, please clarify.Yes, that was a poorly placed citation. We deleted the first sentence of the paragraph (which contained the citation in question) and think the paragraph reads better now.Other points to consider:A further experiment that was suggested was to investigate the distance between TOG domains and the basic region. It is unclear if the length/sequence of the linker connecting the TOG domain to the basic region is important for the activity. As this is a key point additional experiments are needed. Could the linker length be varied? Aligning sequences across different organisms may be helpful in the design of such constructs. Including these alignments in a supplementary table may also useful to readers. However although this would strengthen the paper, we see this as potentially beyond the scope of this paper, and leave this to your discretion.We include one new experiment to begin addressing this question . We purified a Stu2 variant containing a 16 amino acid flexible spacer (GSSGGSSSGSSGGGSG) in between the end of TOG2 (residue 560) and the start of the basic region (residue 561). This ‘spacer variant’ Stu2 displayed polymerase activity, but was demonstrably weaker than wild-type (or even TOG1-TOG2*) in terms of end-binding affinity and maximal fold stimulation of elongation. Along with the TOG1-TOG1 data described above, these new spacer data provide additional support for our claim that proximity between the basic region and a TOG domain is required.The Reviewing Editor was also concerned about the extent to which Stu2 is processive and whether their experiments to measure the end-residence time of Stu2 are the right ones.Thanks for the chance to clarify this. In retrospect we agree that we stumbled through our presentation of this important point.With 5 nM Stu2, you measured an end-residence time of ~2 seconds for a microtubule that is growing at ~0.5 μm/min. That growth rate converts to ~13 dimers per second per end, or 1 dimer per protofilament per second. This means that 2 second residence time isn't much for a protein with 4 TOG domains in the dimer. Although you point out that XMAP215 stayed on the end for ~4 seconds, the growth rate in those XMAP measurements was 3 μm/min or 82 dimers/sec/end. In other words, one could argue that Stu2 is not processive. Within those 2 seconds, it deposits its 4 tubulins and leaves.But you are measuring the end residence time at 5 nM Stu2, a concentration which is basically the same as the "uncatalyzed rate". It might look different if you measured the residence time at 100 or 200 nM Stu2, when the microtubule is growing 5 times faster. Either the end-residence time will be much shorter (~0.4 seconds) or the processivity will be much higher. Those experiments are challenging because you need a small amount of tagged Stu2 alongside a higher concentration of untagged Stu2, or maybe you need to do FRAP and measure recovery rates.We include new data about processivity where the Stu2 concentration is much higher (200 nM), and consequently much of the observed elongation can be attributed to the presence of Stu2. We used a mixture of 5 nM Stu2-GFP and 195 nM untagged Stu2 – this ‘spiking’ allowed us to retain ~single molecule sensitivity with much higher overall levels of Stu2. The distribution of end-residence times we measured for 200 nM ‘spiked’ Stu2 are very similar to what we measured for 5 nM Stu2-GFP. Thus, the end residence times do not decrease as elongation rate increases. This supports the claim that Stu2 is a processive polymerase .This point is important for how you interpret the high affinity of Stu2 for microtubules after tubulin release. You argue the high affinity is a ratcheting mechanism that explains processivity. That can't be the main interpretation if there is no processivity.Here is a simple calculation analogous to the one presented in the Brouhard 2008 paper about XMAP215. At 200 nM Stu2 the growth rate is 2.1 μm/min, which is 1.8 μm/min faster than the uncatalyzed growth rate of 0.3 μm/min. 1.8 μm/min of elongation attributable to Stu2 equates to ~49 dimers/second, or 98 dimers added during the ~2 second residence time for Stu2. We estimate there are about 6 Stu2 molecules on the end at saturation, so assuming they are all active we get 98/6 = 16 dimers added per Stu2. This is a similar order of magnitude to the ~25 dimers added per XMAP215 reported in Brouhard et al., 2008.These new data allow us to discuss questions about end residence and processivity in more detail than we had in the initial manuscript.The amount of Stu2 at the microtubule end seems to saturate before the growth rate effect saturates. More specifically, the Stu2 signal at the end hits max at 20 nM while the growth rate saturates at 60 nM. The authors describe these concentrations as equivalent, and I get that, but our working concentration range is 0-->200 nM effectively, so in fact it's a large difference. we wonder if this indicates that the tip structure of the microtubule end determines how effectively Stu2 can do its work. This might explain why the processivity numbers are so low – maybe Stu2 is not effective on a blunter microtubule end but works most effectively on an end that is tapered?As we explained above, processivity only appeared to be low because we presented that section poorly. With the inclusion of new data and a better Discussion, we think the motivation for this question likely no longer applies.Please also discuss a model for how the proposed 'antagonism' between the basic region and the TOG domains could work.The mechanistic origin of this antagonism remain mysterious to us. We added some speculation about how it might be working in the Discussion.