Pathological narcissism is a severe mental health condition that includes disturbances in interpersonal functioning. Interpersonal difficulties by those affected include aggressive, domineering, cold and coercive behaviours which often result in strong negative reactions from others. We sought to examine the moment-to-moment patterns that emerge within close relationships between intimate partners and family members.N = 15) were romantic partners (73.3%) and family members (26.6%) in a close and long-term relationship (+ 10 years) with an individual with pathological narcissism. Participants told verbatim relationship narratives involving five narrative interactions with their relative with pathological narcissism and five narrative interactions with others. Transcripts were coded using the using Core Conflictual Relationship Theme method. Participants also completed three versions of the Relationship Questionnaire, reporting on 1. their relationship style ‘in general’, 2. their relationship style ‘with their relative’ and 3. the relationship style of their relative.Participants . While the identified wishes  were consistent between relative and non-relative narratives, there was significantly higher disharmony and lower harmony in narratives involving relatives with pathological narcissism. Described disharmony in these relationships involved the relative’s rejecting, subjugating and attacking behaviours, and participants rejecting and withdrawing behaviours. There was a prominent deactivation of participants attachment system when interacting with their relative with pathological narcissism, endorsing predominately dismissing relationship styles. Individuals with pathological narcissism were similarly rated as predominately dismissing.Together, these results reflect the cycles of interpersonal dysfunction for individuals with pathological narcissism and their partners and family members. Treatment implications point to the risk of therapists withdrawing and dismissing a patient with high pathological narcissism in the countertransference. Strategies to monitor and manage these core relational themes in treatment remain a challenge. Diagnostic and Statistical Manual of Mental Disorders One avenue for understanding interpersonal dysfunction for individuals with pathological narcissism has been in the treatment context, given documented difficulties in establishing an effective therapeutic alliance with patients with narcissistic preoccupations , 7. The This study aims to extend this research by investigating the “dysfunctional modes of relatedness” Indeed, recent research on pathological narcissism highlights the complex interactions between perceptions of self and other, and related affective processes with corresponding shifts in mentalizing modes or defensively split object relations . For insThis study seeks to understand patterns of interpersonal functioning for individuals with pathological narcissism and their partners and family members. For this research, partners and family members will be referred to as ‘participants’, individuals with pathological narcissism will be referred to as the ‘relative’ and others will be described as ‘non-relatives’.Given the documented interpersonal dysfunction identified for individuals with pathological narcissism , 3 and tParticipants were partners and family members in a close relationship with an individual with pathologically narcissistic traits. All participants provided written informed consent for their responses to be used in research, following institutional review board approval. Participants that had taken part in previous research ( relative’ with pathological narcissism, participants reported a significantly decreased ‘secure’ relationship style t (14) = -2.6, p = 0.019. These results are displayed in Fig. Participants also completed an informant version of the RQ, reporting on the perceived style of their relative with pathological narcissism. Continuous ratings indicated a significant difference between relatives ‘dismissing’ and ‘secure’ scores This study examined the described interpersonal style and patterns of interaction between an individual with pathological narcissism and informant participants. While relationship wishes  were not significantly different between groups, narratives with relatives with pathological narcissism had significantly greater disharmony, involving instances of relatives attacking, rejecting and subjugating behaviours, and participants rejecting and withdrawing behaviours. Overall, narratives with non-relatives typically involved equal instances of harmony and disharmony, where relationship conflicts were satisfactorily resolved, and relationship wishes were fulfilled. In contrast, narratives with relatives with pathological narcissism involved escalating relationship conflicts, whereby both participants and relatives became increasingly conflictually entrenched and disconnected, and relationship wishes remained unfulfilled. Further, when interacting with their relative with pathological narcissism, participants rated their relationship style to be significantly less secure, and more dismissive, and similarly rated their relative as predominately dismissive.These results provide meaningful examples of interpersonal patterns whereby both participants and relatives became locked in dysfunctional modes of relatedness. Interestingly, the prevalence of wishes was not significantly different between relative and non-relative narratives. This is perhaps not surprising as early writings and findings regarding CCRT elements found that wishes are stable across relationships as “apparently, one’s wishes, needs and intentions in relationships are relatively intractable” . This fTypically, narratives involving individuals with pathological narcissism were more concrete, included non-mentalising descriptions of behaviour, and ended with unresolved relationship ruptures. For example,“While visiting my relative at his home, he made several insults to my appearance including my weight, hair style and colour, and clothing. I walked away. He then insulted my children and used several inappropriate racial epithets towards them. I got my family up and we left.” (Participant #24).This is in contrast with narratives involving non-relatives, which were typically more reflective, involving consideration of the others mind and perspective, and in which relationship ruptures were reconciled in mutually satisfying ways. For example,“I have a co-worker whom I respect greatly. We were co-teaching, but she had been out of town for some time. During this time, I had run the class by myself and had gotten in the mental habit of thinking it was my class. When she got back, she said she felt that I had put her in the role of being an assistant instead of a co-teacher. I reviewed things I said to the students, and I realized she was right. I apologized to her and made sure we had equal responsibility from then on.” (Participant #9).Interpersonal dysfunction is known to be a highly prevalent feature of pathological narcissism , involviTwo potential implications of the current research are presented. First, broadly, participants can be described as responding to their relative with a deactivated attachment , likely A number of limitations should be considered in the interpretation of this study. First, while the included sample is adequate for CCRT methodology  as well Kealy and Ogrodniczuk  outline


Deformations of conventional solids are described via elasticity, a classical field theory whose form is constrained by translational and rotational symmetries. However, flexible metamaterials often contain an additional approximate symmetry due to the presence of a designer soft strain pathway. Here we show that low energy deformations of designer dilational metamaterials will be governed by a scalar field theory, conformal elasticity, in which the nonuniform, nonlinear deformations observed under generic loads correspond with the well-studied—conformal—maps. We validate this approach using experiments and finite element simulations and further show that such systems obey a holographic bulk-boundary principle, which enables an analytic method to predict and control nonuniform, nonlinear deformations. This work both presents a unique method of precise deformation control and demonstrates a general principle in which mechanisms can generate special classes of soft deformations. Explanation is still lacking for the exotic nonlinear, nonuniform deformation of mechanism-based materials. Here the authors present an analytical framework for a canonical mechanism that can not only predict, but also precisely control deformation in these materials. Often these features rely on the careful arrangement of the cuts, pores and folds to emulate a mechanism, which is a special pathway of deformation that enables the metamaterial to change shape at a very small  energy cost. For example, the canonical rotating square (RS) mechanism, which consists of perfectly hinged rigid squares, enables a uniform dilational motion  simulations which preserve the intricate pore structure (see “Methods”). While the detailed microscopic strains may contain significant shear, we use the displacement of the square centers to extract coarse-grained shear strain and dilational strain fields, shown in Supplementary Fig. res Fig. f. The lor =  ↔ x + iy ≡ z which has previously been applied to elasticity in a variety of contexts18. In our case, it has the added utility that any exact conformal deformation takes the convenient formf(z) is the deformed position of the material element initially located at z and the complex coefficients {Cn} define the map. The ability to express such a map in this reduced series form, turns the search for the conformal map closest to our data into a linear algebra problem which is readily solved. Using only the first 20 Cn coefficients, we use this procedure in Fig. conf (about 1%) of the observed displacements. Note here that, to appropriately assess the error in many different approximations of the observed square block displacements, we employ the fraction of variance unexplained Δ2. This measure is equivalent to the mean square displacement error normalized by the mean square magnitude of displacements; an explicit formula for this is given in Supplementary Note Cn is displayed in Supplementary Fig. z such as The observation of approximately conformal local behavior suggests that deformations of the elastic RS metamaterial may also correspond globally to a conformal map. This motivates an elegant formulation expressing positions in the plane as complex numbers entclass1pt{minimaentclass1pt{minimaα is a (nonlinear) spatially varying field describing the dilation factor of the structure relative to its equilibrium, while s1 and s2 are the (linear) coarse pure shear and simple shear respectively, ℓ is the width of a hinge and a the width of a square piece. G1,2(α) are shear moduli, M(α) captures the dilation energy density and C (the metric of deformation): when we choose to orient the axes of our reference space along the vectors connecting square centers to those of their neighbors they take the formσ(1) and σ(3) are the first and third Pauli matrices. While alternate analytic approaches to the nonlinear strain of anisotropic media have been established , this formalism is particularly convenient for the RS metamaterial, for which any additional terms such as couplings between dilation and shear are excluded by scaling arguments and by the the orthotropic symmetry (p4g) as presented in Supplementary Note 17 and two dimensions12, and another theory for a bistable dilational material13, this nonlinear analytic form, which centers the dilational mechanism and its gradients, has not been proposed previously. While this energy function holds a rare level of insight into nonlinear deformation, it is still quite difficult to solve analytically.Given this evidence that the RS metamaterial responds to loading with a near-conformal deformation, we present an elastic energy functional,l/a ≪ 1 (i.e. the soft mechanism limit) and the material sample is composed of very many unit cells . Here, the second two terms in equation  become f: z → f(z) describing the local dilation and rotation, with s1 = s2 = 0 and 2 ≈ 0.0001−0.01 for hinges with ℓ/a = 0.005. This result showcases both the accuracy of conformal elasticity and the mathematical convenience of conformal maps.With the nonperturbative part of the energy penalizing only shear, and the set of conformal maps forming a degenerate set of ground states, it would seem that an infinite number of conformal maps are equally likely to arise in response to generic loading such as that in the FEM simulations and experiments of Fig. mentclass2pt{minimM and Solving equation  analyticThe bulk-boundary correspondence can also be used for prescribing on-demand deformations. Given a target deformation field of some section of material, we can determine the suitable actuation pattern by simply examining the local target dilation at the material boundary. We illustrate this with three different actuation patterns in Fig. y > 0), and the dilations along the boundary (x-axis) constrained to satisfy α = α0(x), there is again only one allowed a pattern of dilation, which takes the formw(k) is the Fourier transform of the function w(x) = ln(α0(x)). In this form, we can see that the dilation  decays into the bulk exponentially according to the lengthscale of variation of the boundary dilation. For instance, when w(x) = w0cos(k0x), we will have While the deformations of Fig. 22, so are conformal deformations to dilational metamaterials. Consequently, dilational materials derived from a variety of different metastructures26 including fractal RS structures26, disordered “pruned” networks27, nanoscale28, and three dimensional generalizations30 will similarly have mechanical response controlled by a set of conformal soft modes. These alternate architectures provide the possibility for greater ranges of dilation24, opening the door for even more dramatic soft deformations such as pictured for the kagome lattice in Fig. The intuitive concept of soft modes that are locally composed of a spatially varying mechanism is not confined to the RS mechanism explored here. The conformal modes explored here arise because they are the Nambu-Goldstone modes associated with the local dilational symmetry, rather than from the details of the specific microstructure. We therefore propose the analogy: as isometries are to thin elastic sheetsNote, by example, that many possible loads applied to thin sheets (e.g. applying an in-plane stretch to a flat sheet) are incompatible with isometries, and an isometric soft mode theory must break down there. Similarly, a variety of overly strict loads will be incompatible with conformal deformation and our conformal elasticity theory must similarly break down. For instance, this will happen when attempting to constrain the displacements, rather than dilations all along a closed boundary. Conveniently, we have identified two particular scenarios with either a finite number of sufficiently spaced point displacements, or with dilations controlled along a closed boundary, each of which contain a broad variety of loading possibilities which are guaranteed to be compatible with a conformal map, and are therefore governed by our elasticity theory. Identifying theories that apply beyond the soft conformal response will provide an interesting avenue for future work.31 and that this may become a fundamental principle for mechanism-based metamaterials, with potential applications from footwear to soft robotics. In addition, topologically polarized systems32, which have additional mathematical structure controlling an additional set of exotic boundary modes, necessarily break the symmetry of the mechanism strain pathway that allows conformal modes and the notion of mechanism compatibility may be extended to their nondilational mechanisms while incorporating topological notions. Exploring how these new classes of soft modes may obey, e.g., a generalized bulk-boundary correspondence may yield fruitful connections to exotic field theories33 as well as black hole and string theories where holographic principles already play a vital role.We suggest that a broad class of generic mechanisms not confined to pure dilation will also generate families of soft modes that govern material response, as were indeed observed in so-called “kirigami” structuresx → X(x) has deformation tensor xi → Fijdxj. To correspond locally to a rotation of the mechanism field, the deformation tensor must be proportionate to a rotation matrix. We seek solutions in which the rotation angle ϕ and the dilation factor α vary over space, so that F = αR(ϕ). However, this generates nontrivial restrictions because of the requirement that the deformed state not tear, i.e. that ∮dX = 0 along any path. By applying Stokes’ theorem, this leads to the standard geometric or kinematic compatibility conditions:We analytically generate the deformations of two-dimensional metamaterials that locally resemble the uniform mechanism and a rotation and hence eliminate the dominant contributions to the elastic energy, taking advantage of a map between the plane of deformation and the complex plane. In general, a deformation map i the imaginary unit. Enforcing the conditions z = (1/2)(∂x − i∂y), This generates two independent conditions on the two fields, suggesting a unique solution subject to certain boundary conditions. However, it becomes convenient to define new variables By summing the second compatibility condition with (−i) times the first, we find, following some algebra, that compatibility is equivalent to the complex condition μ = 0.333 MPa*m, bulk modulus K0 = 16.7 GPa*m and plane strain conditions with hybrid quadratic triangular elements (Abaqus type CPE6H). We construct the mesh so that the thinnest parts of the samples are at least two elements across. We perform two types of simulations (i) on the metamaterials unit cells with periodic boundary conditions, on which we perform a low strain static test (5 × 10−6)) and (ii) on the full structure, on which we perform static nonlinear analysis.For the 2D finite-element simulations, we use the commercial software Abaqus/Standard. We use a neo-Hookean energy density as a material model, a shear modulus 34. We perform four types of simulations to extract the macroscopic linear bulk modulus G1(α = 1), G2(α = 1). In order to extract K and G1, we apply biaxial compression ε12 = 0 and pure shear ε12 = 0 to a unit cell with periodic boundary conditions. K and G1 are each extracted as a slope in linear fitting G2 is extracted using the identical procedure as for G1 applied to a unit cell that is rotated by π/4. We examine a unit cell with a = 12mm, pretwist θ0 = 0.39 and hinge thickness ℓ = 0.1 mm, finding  ~ Pa*m. These modulus measurements are essential for obtaining the perturbative energy minimizing prediction of deformation, summarized briefly in the main text and given in more detail in Supplementary Note In the simulations (i), to implement periodic boundary conditions, we define constraints on the displacements of all of the nodes at the horizontal and vertical boundaries of the unit cell and impose displacement using virtual nodesℓ = {0.02, 0.033, 0.055, 0.092, 0.155, 0.258, 0.431, 0.719, 1.199, 2.0} mm.In the simulations (ii), we use three kinds of boundary conditions, dipole, bending and pure shear, by imposing displacement of specific nodes, as described in Fig. a = 4.8 mm connected by hinges of thickness ℓ = 0.2 mm using a Connex 500 Stratasys 3D printer and the proprietary Stratasys Agilus 30 material (30 Shore A). This material is viscoelastic, its Young’s modulus at short times is E = 3.3 MPa and E = 0.6 MPa at long times . For imaging purposes, we 3D printed black square-shaped pads (3.6mm) on one edge of the sample .We 3D printed an elongated meta-beam of length 306 mm, width 64 mm and height 40 mm, consisting of a checkerboard of lattice of 48 × 10 squares of side μm). We attribute the strongly different behavior observed between compression and decompression, to a combination of frictional, viscoelastic and self-adhesion effects.The sample was tested under two sets of nonuniform boundary conditions, (i) by a foot-shaped indenter, as depicted in Supplementary Movie x(1), dx(2)) and target spaces (dX(1), dX(2)), we may infer the deformation tensor x(1) and dx(2) are orthogonal, the recipe is simplyC = FT ⋅ F. Nonlinear dilation and shear strain magnitudes may be obtained from the invariants of the metric tensor viaTo extract nonlinear measures of coarse dilation and shear strain magnitudes from the deformation of RS-based structures, we track the square center displacements. Using the vectors connecting square centers to neighboring square centers as the infinitesimal material vectors in the reference  which most closely maps a set of np points {zi} (expressed in complex form) to final positions f  ≡ f(z) − z are the displacements proposed to fit the data. The functional Δ2, quantifies the “fraction of unexplained variance” in the displacements, and constitutes a general method to quantify the amount of the deformation captured by a candidate conformal function, used in Fig. To evaluate the accuracy of the fit, we define a similar function to the error in equation 15\documef: z → f(z) which will arise from a discrete set of M applied dilations {αk} at a corresponding set of locations {zk} along the boundary of a simply-connected planar domain. Here, Ak and after In our bulk-boundary method for predicting deformation, we are able to infer the conformal deformation f(z) will be conformal, so is the function N should be significantly less than half the number of boundary points M in order to avoid overfitting. Note now that since g(z) = log(α) + iϕ, the real part of g alone determines the dilation field. We may therefore infer the g that fits the boundary conditions by minimizing the errorDn = An + iBn yielding the equationsg to a linear algebra problem which may be readily solved using built-in tools in, e.g., Mathematica. Noted also in Supplementary Note B0 = ϕ0  are zero throughoutand this row-column pair must be removed before numerically solving.Because g(z), specified by the coefficients {Dn}, is used to reconstruct the z-derivative of the conformal map via u(z) = f(z) − z, shown in main text Fig. As described in Supplementary Note Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3


N-glycosylated proteins, with an N-terminal domain, one transmembrane domain and a bulky extracellular domain (ectodomain) that belongs to the α-amylase family. The heavy subunits are covalently linked to a light subunit from the SLC7 family, which is responsible for the amino acid transport activity, forming a heterodimer. The functions of 4F2hc and rBAT are related mainly to the stability and trafficking of the HATs in the plasma membrane of vertebrates, where they exert the transport activity. Moreover, 4F2hc is a modulator of integrin signaling, has a role in cell fusion and it is overexpressed in some types of cancers. On the other hand, some mutations in rBAT are found to cause the malfunctioning of the b0,+ transport system, leading to cystinuria. The ectodomains of 4F2hc and rBAT share both sequence and structure homology with α-amylase family members. Very recently, cryo-EM has revealed the structure of several HATs, including the ectodomains of rBAT and 4F2hc. Here, we analyze available data on the ectodomains of rBAT and 4Fhc and their relationship with the α-amylase family. The physiological relevance of this relationship remains largely unknown.It is known that 4F2hc and rBAT are the heavy subunits of the heteromeric amino acid transporters (HATs). These heavy subunits are  L-amino acid transporter (LAT) subunit from the SLC7 gene family by a disulfide bridge  or ectodomain (ED)  heterodimerize with 4F2hc and two  with rBAT, thereby rendering a range of different amino acid transport activity . As . As 5]. 8-barrel fold, linked to the TM domain by the eleven-residue neck indicated above ed above a,b. At ted above b that is0,+AT and a single homodimer of rBAT (8-barrel of the α-amylase family (673-Cys685) protruding from domain C  has a conserved Ca2+ site in the same position as rBAT, stabilizing domain B [2+ binding site in rBAT-ED stabilizes the interaction of the loop Aβ3-Aα3 with the next one in the (β/α)8-barrel, the Aβ4-Aα4 (2+ interacts with and stabilizes them as a single domain above the (β/α)8-barrel    as in human (glycosylated) rBAT-ED [N-glycosylation is also observed in some α-amylases of mammals [N-glycosylation in these enzymes is related to stability and also serves as a modulators of the enzymatic activity, but no structural pattern has been found between them [The cryo-EM structures of rBAT and 4F2hc expressed in mammal cells showed the characteristic glycosylations in some Asn residues of the ED. The 4F2hc-ED shows four Aβ8-Aα8) b. In conomain C) c. These omain C) b,c. Inde protein . Unexpecerodimer . However rBAT-ED . N-glyco mammals , plants  mammals , and eveeen them .E. coli (without N-glycosylations) or in mammalian cells (N-glycosilated), do not show large differences, even near the glycosylation sites, thus indicating that N-glycosilations in 4F2hc are not needed for proper protein folding. Moreover, 4F2hc-ED produced in E. coli shows high stability [The structures of 4F2hc-ED produced in tability  and eventability .Cryptotermes secundus and 4F2hc-ED (residues 219–631) shares 26% SI with α-amylase of Pseudomonas indica (α-amylases with higher scores in BLAST-P against nr-database). The structure conservation between these ectodomains and α-amylases is even higher, as can be seen superimposing the structures of rBAT-ED and of the α-glucosidase Cqm1 (PDB ID 6K5P) [2 RMSD, sharing 40% SI. Indeed, the crystal structure of 4F2-ED was solved by molecular replacement using as a model an oligo-1,6-glucosidase structure (PDB-ID 1UOK), sharing above only 25% sequence identity [The rBAT-ED (residues 117–685) shares around 40% sequence identity (SI) with maltase-1 isoform-X3 from ID 6K5P) ,which th8-barrel fold in domain A followed by a domain C with a β-sandwich fold  are not conserved in human rBAT-ED ich fold b,c. rBATich fold c,d. In ad Asp449 d, as can. cereus d. Moreov rBAT-ED d. Phe297 residue . Finally62 in HaG, has a conformation not compatible with the position of maltose in the binding site of HaG , common in all the α-amylase family, are missing. Therefore, 4F2hc-ED is not expected to have α-glucosidase activity. In this regard, null detection of activity was found in a detailed screening using d-glucose, d-galactose or d-mannose derivatives of 4-methylumbelliferone as substrates [Despite the strong evidence of the sequence and structural homology of the EDs of rBAT and 4F2hc with α-amylases, no α-glucosidase activity has been demonstrated for either protein ,33. In 4bstrates . In all,bstrates  and Edembstrates . Despitebstrates . It has bstrates . Moreovebstrates , ICAM-1 bstrates , CD-147 bstrates  and CEA-bstrates , have be0,+AT solubilized in detergent (glyco diosgenin 0.02%) failed to demonstrate α-glucosidase activity [In contrast to 4F2hc-ED, rBAT-ED conserves the three catalytic residues of the α-amylase family d. Nevertactivity . StudiesIn conclusion, 4F2hc and rBAT, the heavy subunits of heteromeric amino acid transporters, have an ED with sequence and structure homology with α-glucosidases that can be classified as members of the GH13 family.8 barrel as a domain A followed by domain C with a β-sandwich fold. In addition, rBAT has also domain B protruding from domain A and formed by two different loops, Aβ3-Aα3 and Aβ4-Aα4. This domain B is stabilized by a disulfide bridge and a Ca2+ ion, similarly to in α-amylases. This domain B is key for the functional oligomeric state of rBAT-b0,+AT as superdimers.rBAT-ED and 4F2hc-ED have a (β/α)The characteristic active site of α-amylases, including key catalytic residues, is fully conserved in rBAT-ED and not in 4F2hc-ED. However, α-glucosidase-related activity  has not yet been proven for any of these two heavy subunits. Further experiments are needed to unravel the physiological role of the homology of rBAT-ED and 4F2hc-ED with α-amylase family members.


Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as sensor platforms. Neural network pruning is an efficient way to design a lightweight model from a well-trained complex deep neural network. In this paper, we propose an evolutionary multi-objective one-shot filter pruning method for designing a lightweight convolutional neural network. Firstly, unlike some famous iterative pruning methods, a one-shot pruning framework only needs to perform filter pruning and model fine-tuning once. Moreover, we built a constraint multi-objective filter pruning problem in which two objectives represent the filter pruning ratio and the accuracy of the pruned convolutional neural network, respectively. A non-dominated sorting-based evolutionary multi-objective algorithm was used to solve the filter pruning problem, and it provides a set of Pareto solutions which consists of a series of different trade-off pruned models. Finally, some models are uniformly selected from the set of Pareto solutions to be fine-tuned as the output of our method. The effectiveness of our method was demonstrated in experimental studies on four designed models, LeNet and AlexNet. Our method can prune over 85%, 82%, 75%, 65%, 91% and 68% filters with little accuracy loss on four designed models, LeNet and AlexNet, respectively. Recently, deep neural networks have achieved significant development with the innovations of computing equipment, especially for GPU-based computing. The excellent performance of deep neural networks has led to their applications in many fields such as computer vision, speech recognition and natural language processing . For praFactually, studies ,9 have sWe still face some issues for convolutional network pruning. Firstly, the unstructured pruning methods can obtain a sparse model, but it is also hard to be deployed on resource-limited devices without specific sparse computing algorithms. For structured pruning methods, the key problem is how to distinguish which group of parameters can be removed. Although some studies have proposed different metrics of importance estimation, there is still no uniform and comprehensive pruning standard. Secondly, handcraft technology-based pruning methods may not select optimal pruning parameters. Moreover, it is hard to balance the model scale and model performance, or we will incur significant time and resource costs to obtain a good trade-off between the model scale and model performance. Thirdly, compared with one-shot pruning, although iterative pruning methods are can obtain better results with more ease, iterative pruning methods always have larger computation complexity and would cost more time. The time cost of a human is very expensive for iterative pruning methods, especially for designing a series of similar models. Whether it is iterative pruning or one-shot pruning, we need to design each model independently for each device.In this paper, we propose an evolutionary multi-objective one-shot filter pruning method (EMOFP) for designing a lightweight convolutional neural network. In EMOFP, there are three phases: obtaining a well-trained CNN; evolutionary multi-objective filter pruning (EMFP); and a fine-tuning pruned model; these phases are implemented only one time. It is important to note that the key of the EMOFP is the second phase. In EMFP, we build a constraint multi-objective filter pruning problem, in which two objective functions represent the pruning ratio and the accuracy of the pruned model, respectively. Then, a non-dominated sorting-based evolutionary multi-objective algorithm is used to solve the above filter pruning problem. Finally, for a series of different trade-off pruning solutions of EMFP, we uniformly select some pruned model to the third phase, fine-tuning the selected pruned models. In total, our EMOFP automatically completes the filter pruning task without extra human hand-craft and pruning and fine-tuning only once, and provides a series of pruned models with a different trade-off between the model scale and model performance.The remainder of this paper is organized as follows. In X denotes the dataset on which to train and fine-tune. The architecture of a neural network includes the configuration of a network’s parameters and the set of operations which consists of convolutional units, activation functions, pooling, batch normalization, etc. Therefore, we can define a neural network model as the particular parameterization of an architecture. For example, for a specific parameter W, the model can be denoted as W. Generally, we can obtain W. In practice, rather than using an explicit mask, the pruned parameters of W are fixed to zero or removed entirely.According to the literature ,12, we dX to obtain the original complex model. In the second phase, we prune the original complex model with some different pruning strategies to obtain the pruned model. In the third phase, in order to maintain the accuracy of the original model, we fine-tuned the pruned model on the dataset X. Furthermore, for iterative pruning methods, the second and third phases will be executed alternately for N iterations. When N is equal to 1, this framework represents a one-shot neural network pruning method.Algorithm 1: General Framework of Pruning and Fine-TuningA general framework of neural network pruning can be summarized in Algorithm 1. The neural network pruning task can be divided into three phases . In the Most neural network pruning methods focus on proposing or improving pruning strategies in the second phase. For example, an early approach to pruning was biased weight decay which is based on weight magnitude . The optm means the number of objective functions, and n denotes the dimension of decision variable. In general, the objectives in MOP are in conflict with each other. Therefore, for solution Multi-objective optimization is used to solve multi-objective optimization problems (MOPs), which consist of more than one objective function and all of the objective function needs to be optimized simultaneously. Therefore, an MOP can be stated asGenerally, it is very difficult to obtain whole Pareto optimal solutions. Therefore, multi-objective optimization needs to obtain a uniformly distributed Pareto front which can represent the whole solutions approximately. Nowadays, most multi-objective algorithms are designed based on population optimization, especially with evolutionary algorithms, such as the non-dominated sorting genetic algorithm (NSGA-II) , multi-oMulti-objective optimization has been widely used to solve machine learning problems. For example, using multi-objective to sparse reconstruction was proposed in . In 34,,34,35, mIn this section, we introduce the proposed EMOFP in detail. Firstly, the overview framework of multi-objective one-shot filter pruning is presented. Then, we introduce the mathematical model of filter pruning, especially the two conflicting objective functions. Moreover, an evolutionary multi-objective algorithm was used to prune the filters of the convolutional neural network in detail. Finally, we fine-tune the pruned model to maintain the accuracy as much as possible.i-th convolutional layer can be represented as the product of the input tensor and filters. Taking a convolutional layer as an example, in the second phase, we initialize a population which consists of N different filter pruning schemes. The virtual filter represents the filter that will be removed and the solid filter indicates the filter that will be retained in In our EMOFP framework, the main works can be divided into three phases. Firstly, we obtain an original well-trained convolutional model, which may be trained by ourselves or is a public well-trained model. Secondly, we prune the original model by evolutionary multi-objective filter pruning method, and obtain a series of trade-off pruned models. Lastly, models from the second phase would be fine-tuned to improve the performances of these models. Therefore, our method outputs a series of different trade-off lightweight convolutional models. A detailed framework is shown in W denotes the filters of a convolutional model in which X means the dataset used to train and fine-tune. Moreover, the W and pruning binary mask In order to parameterize the filter pruning for convolutional neural networks, we assume that In the filter pruning task, we not only need to obtain as simple neural networks as possible, but we also need to retain the performance of obtained neural networks as much as possible. Therefore, we designed the two following objective functions:C denotes a constant, and The numerical range of a first objective function Algorithm 2: Pseudocode of evolutionary multi-objective filter pruningFor the above multi-objective filter pruning model, we use an evolutionary multi-objective filter pruning (EMFP) algorithm based on NSGA-II to optimize it. The pseudocode of EMFP is shown in Algorithm 2. In algorithm EMFP, we binarily encode the pruning masks as individuals of an evolutionary population, and genetic operations, such as crossover and mutation, are used to generate offspring, whilst finally the nondominated sorting and crowding distance-based selection are applied to update the population and Pareto front. We will then introduce some detailed operations in EMFP.N times to generate the population, and the fitness calculation is based on Equation , and then uniformly select K models from the model set. For selected pruned models, we fine-tune them with the strategies of the data augmentation and auto-reduced learning ratio.In the model fine-tuning phase, we need to select some valuable pruned model before fine-tuning the lightweight neural networks, because the EMFP algorithm outputs a series of trade-off pruned models. For selecting suitable pruned models, we followed the rule of uniform selection. For example, if we need to select N and G, respectively. The computational complexity of initialization, the nondominated sort and calculating the crowding distance are K stands for the number of pruned models selected for fine-tuning, the computational complexity of the fine-tuning phase is Before analyzing the computational complexity of the proposed EMOFP, we assumed some computational costs of special operators. Assuming that the computational cost of the neural network training is In this section, we demonstrate the performance of the proposed EMOFP with experimental studies. Firstly, we introduce the experimental setting and related convolutional neural networks and datasets. Secondly, the overall experimental results—especially the comparison results—are shown. Lastly, we present and analyze the experimental results of our EMOFP on these models in detail.In our experimental studies, we designed four convolutional models which have a different number of convolutional layer and the same fully connected layers. Moreover, we also apply our EMOFP on common yet efficient models, LeNet and AlexNet. Simple descriptions of used neural network models are shown as follow.Conv1: Conv1 is a simple convolutional neural network which consists of a convolutional layer and two fully connected layers. In the convolutional layer, there are 64 filters with the size of Conv2: Conv2 is a convolutional neural network which consists of two convolutional layers and two fully connected layers. The first convolutional layer has 32 filters with the size of Conv3: Conv3 is a convolutional neural network which consists of three convolutional layers and two fully connected layers. The number of filters of each layer are 16, 32 and 64, respectively; the filter size of all filters is Conv4: Conv4 is a convolutional neural network which consists of four convolutional layers and two fully connected layers. The number of filters of each layer is 16, 32, 64 and 64, respectively; the filter size of all filters is LeNet: LeNet  is a claAlexNet: AlexNet  is a claDetailed model variants, such as configurations, accuracy and used datasets are presented in Firstly, we define some evaluation metrics to measure the pruned model, which consist of the error, relative error, filter compression ratio and floating point operations (FLOPs) of the pruned model. A detailed description of these is shown as follows.Error: The error of the neural network byRelative error (RE): We define relative error of pruned model asCompression ratio (CR): We define the compression ratio of filter pruning asFloating point operations (FLOPs): FLOPs can effectively measure a model’s computation resources, and it is always used to measure model complexity in practical applications.For exploring filter pruning effectiveness, all experiments simply remove the whole filter with fixed fully connected layers. In our experiments, we use the pymoo  package The comparison methods used in this paper are the  pruning . The difBecause EMOFP is a one-shot pruning method, the comparison methods will also use a one-shot pruning framework for fair comparison. Moreover, EMOFP provides a series of trade-off solutions, and comparison methods only obtain a pruning result in one running. Therefore, in order to better compare, we select three different trade-off solutions which are located in the head, middle and bottom of the Pareto front, respectively. Then, the comparison methods pruned the model with same pruning ratio with selected solutions. Finally, for each model, we provide three different results with a different pruning ratio for each method.In this part, we present the experimental results of our designed models. The pruning results on Conv1, Conv2, Conv3 and Conv4 are shown in First of all, from Secondly, Thirdly, the comparison results and the Pareto front are shown in Finally, we present comparison results and Pareto front on Conv4 in From these results on four designed models, we can make sure that EMOFP provides a series of efficient different trade-off solutions and has better performance than the comparison methods. Moreover, we can also know that with the depth of increasing depth of the model, the pruning performance decreases. For four Pareto fronts, the Pareto fronts of Conv1 and Conv2 are better than those of Conv3 and Conv4. It is well known that the filter pruning problem dimension is increasing while the model becomes increasingly deep. Therefore, the difficulty of the pruning problem is increased. For example, the pruning problem dimensions of four designed models are 64, 96, 112 and 176. Furthermore, the number of filters of each layer is also a restricted condition for optimization, which will be complex due to the increasing number of layers. It is therefore increasingly difficult to find solutions with a large filter pruning ratio, and the results show that the biggest filter compression ratio decreases when the model becomes complex. Moreover, the FLOPs of our pruned models are less obvious than those of the original model, although the FLOPs of EMOFP are not the most competitive. This is because our EMOFP is only optimized for the number of filters and does not take FLOPs into account. Generally, EMOFP surely obtains a lightweight model with acceptable performance.In this part, we will show the experimental results on LeNet, which is one of the most familiar convolutional neural networks. Firstly, we plot the Pareto front of EMFP and the fitness of the fine-tuned models corresponding to Pareto solutions in The comparison results on LeNet are presented in AlexNet was the deepest convolutional neural network used to examine the performance of EMOFP in the experimental studies. Furthermore, the detailed experimental results are shown in We present a detailed comparison of the results on AlexNet in From the experimental results, we observed that the performances of fine-tuned models have some differences although they have the same model structure. The difference between these pruned models before fine-tuning is their weights. Therefore, in this part, we explore the influence of different weights on the pruned model to analyze that the purpose of pruning is searching a suitable lightweight model structure or a pre-trained model which only needs to be fine-tuned. In this experiment, we rebuilt the model with random initialized weight parameters according to the configuration of the pruned model. The experimental results are shown in From In order to exam the performance of the proposed method, we provided a simple practical example about cat and dog classification. Firstly, we randomly selected 1000 cat images and 1000 dog images as training samples, and each RGB image with the shape of K-pruned models were uniformly selected for fine-tuning to promote their performance. Therefore, EMOFP provides a series of different trade-off pruned models instead of a pruned model. Experimental studies of four designed models and two famous models demonstrated that our method can efficiently prune filters to obtain a lightweight convolutional neural network. Compared with the In this paper, we proposed an evolutionary multi-objective one-shot filter pruning method to design a lightweight convolutional neural network. Firstly, EMOFP is no longer an iterative pruning method and only needs to perform pruning and fine-tuning once. Secondly, a multi-objective filter pruning problem was established, which consists of two objective functions and represents the filter pruning ratio and the accuracy of the pruned model. Thirdly, we used a NSGA-II-based evolutionary multi-objective algorithm to solve the above filter pruning problem and obtain a series of different trade-off pruning solutions. Finally, There are still some unresolved issues in this paper: for example, the pruning performance of a larger model is decreased because of the increasing difficulty in optimization. Therefore, in future work, we want to explore more efficient optimization strategies for very deep convolutional neural network filter pruning.


Hepatocellular carcinoma (HCC) is the most common high malignancy with insidious onset, invasive fast-growing, high recurrence rate and fatality. YTH domain family plays essential roles in development of HCC. However, the biological function of YTH domain family in HCC have not been clarified. Here, through evaluating the expression profiles of YTH domain family, we found that upregulated YTHDF1 might be more significant and valuable in development and progression of HCC. There was a strong correlation between YTHDC1, YTHDF1 and YTHDF2 and pathological stage of HCC patients. Kaplan-Meier plotter revealed that HCC patients with high level of YTHDF1 and YTHDF2 were highly related to a shorter overall survival time, and low level of YTHDF1 (p = 0.0017) has an important association with a longer progression-free survival time. Genetic alterations using cBioPortal revealed that the alteration rates of YTHDF3 were the highest. We also found that the functions of YTH domain family were linked to several cancer-associated pathways, including peptidyl-serine modification, peptidyl-tyrosine modification and negative regulation of cellular component movement. TIMER database indicated that the YTH domain family had a strong relationship with the infiltration of six types of immune cells . Next, Ualcan databases revealed that the global methylation levels of YTHDC1 was higher in HCC patients, while YTHDF2 was lower in HCC patients. In conclusion, our findings will enhance the understanding of YTH domain family in HCC pathology, and provide novel insights into YTH-targeted therapy for HCC patients. Hepatocellular carcinoma (HCC) has become one of the most common and aggressive malignant disease to bring about worldwide concern, which ranks as the sixth of morbidity and the third of mortality in all malignant tumors , 2. The Accumulating statistics indicate that among a series of RNA modification adenosines, N6-methyladenosine (m6A) plays the most significant part in modulating numerous cellular processes including cancer biological functions , 6. MeanDespite the YTH domain family plays significant role in cancer development, the systematic analysis of YTH domain family expression profiles and functions in HCC were still insufficient. In our study, we devoted to explore the expression features and potential biological functions of YTH domain family in HCC patients .We used TIMER database to detect the expression levels of YTH domain family members. And the result showed that all of YTH domain family members were significantly raised in various malignant tumors . Then, tThen, we explored the association between the expression profiles of YTH domain family and the pathological stages of HCC patients. As shown in Then, The Kaplan-Meier plotter was used to evaluate the prognostic value of YTH domain family for patients with HCC. The data of overall survival (OS) curves are shown in To further analyze the alteration profiles of YTH domain members in patients with HCC, we conducted a series of surveys as follows. First, we used the cBioPortal database to evaluate the genetic alterations of YTH domain family. As shown in Next, Using HCCDB database to evaluate the associated molecules of YTH domain family, we found that the interactive molecules of all members had altered significantly between tumor tissues and adjacent normal tissues, respectively . AdditioIn our study, we applied the WebGestalt database to evaluate the potential biological functions of YTH domain family in the occurrence and development of HCC. As shown in the Gene Ontology (GO) term analysis , we knowTIMER database was used to explore the relationship between immune cell infiltration and expression profiles of YTH domain family . The expFurthermore, we analyzed the methylation levels of YTH domain family by searching Ualcan database. We found that the methylation levels of YTHDC1 (p < 1E-12) was higher in HCC patients than in normal cases; while YTHDF2 (p = 1.478E-06) was lower in HCC patients . These dYTH domain family members were the m6A “reader” proteins which were firstly identified. M6A modification can be recognized by YTH domain family proteins in a methylation-dependent manner, which is identified by sequence comparison and is found in 174 different proteins expressed in eukaryotes . There aIn our study, according to the TIMER, Wanderer, GEPIA and HCCDB database, we concluded that the expression levels of YTH domain family are different between HCC and normal tissues. Especially, YTHDF1 might be more significant and valuable to be as an oncogene in development and progression of HCC. Additionally, we also performed the relationship between expression levels of YTH domain family and HCC pathological stage. We found that the expression level of YTHDC1, YTHDF1 and YTHDF2 were increased with the HCC stage progressed, suggesting that these three proteins might participate in disease progression in HCC patients. Moreover, HCC patients with high expression of YTHDF1 and YTHDF2 had a poor OS time. HCC patients with high expression of YTHDF1 had a poor PFS value. These databases implied that YTH domain family is crucial for the progression and survival of HCC patients. However, more specific studies about the expression and prognostic values of YTH domain family in patients with HCC needs to be further explored.To better understand the potential mechanism of YTH domain family in HCC, we performed GO and KEGG pathway enrichment analysis to explore the function of YTH domain family. We found that the biological functions of YTH domain family participated primarily in the posttranslational modification pathways, such as peptidyl-serine modification and peptidyl-tyrosine modification. A previous study has shown that serine biosynthesis pathway and tyrosine relative kinase inhibitors play significant roles in the regulation of cancer progress and immunotherapy for HCC , 21. In Immune molecules play a critical role in the regulating tumor responses –24. A grin vitro and in vivo experiments to identify the functions and mechanisms of YTH domain family in HCC. Secondly, the study did not conduct some survey concerning the therapeutic outcome of HCC patients. Thus, the outcomes need to be validated and amended in the future multicenter, large-scale clinical trials. Finally, it is important to explore prognostic effects of the YTH domain family on HCC patients in order to improve their application values in clinic.In the present study, there still exist some limitations. Firstly, the statistics we analyzed were mainly from the databases, therefore, we should present more In conclusion, we comprehensively analyzed the profiles of YTH domain family in HCC from the perspective of bioinformatics, including expression levels, prognostic values and immune response. The results showed the YTH domain family, especially YTHDF1, possessed great potentiality as therapeutic targets and prognostic biomarkers in HCC via regulating multiple mechanisms. These findings might help clinicians design effective therapeutic strategies to improve the treatment effect and prognosis of HCC patients.Wanderer, an intuitive Web tool, which permits real-time access and visualization of gene expression and DNA methylation profiles from TCGA using gene targeted queries . WandereGene Expression Profiling Interactive Analysis (GEPIA) is a database that is designed to help end users fully understand gene expression from a more holistic perspective . We usedHCCDB is a one-stop online database to explore HCC gene expression with user-friendly interfaces . The gloKaplan-Meier plotter is a database assessing the relationship between gene expression with the survival trend of numerous cancer patients . In our cBioPortal provides a user-friendly analysis strategy concerning gene-disease associations on over 200 cancer cases . In our STRING was applied to explore potential protein-protein interactions (PPIs). Furthermore, this database provides all resources concerning the interactive network of multiple proteins . At the GeneMANIA has been applied in scientific research for many years and is very convenient for identifying protein-protein interactive networks . Using GWebGestalt aims to provide users with a better understanding of gene interpretation. Researchers can obtain enrichment results from this database . In thisTIMER is used to evaluate the connection between infiltrating immune cells and cancer cells, which could provide more rational strategies for an improved therapeutic response and prognosis . We mainThe Ualcan data portal can help to identify candidate biomarkers of specific cancer subtypes, with diagnostic, prognostic therapeutic implication . In our Supplementary Figure 1Supplementary Table 1Supplementary Table 2


MLKL phosphorylation by RIP3 is the commitment step of necroptosis execution, which could induce MLKL activation featured as MLKL monomer-oligomer transition. Here, we reported that the dimerization of the MLKL kinase-like domain was the direct consequence of RIP3 triggered MLKL-phosphorylation. Two inter-dimer interfaces were found in the crystal structure of human MLKL. Mutations destroying both interfaces could prevent RIP3-induced MLKL oligomerization and necroptosis efficiently. Moreover, we confirmed MLKL self-assembly by the internal coiled-coil region is necessary for MLKL oligomerization and function. The mutations disrupting coiled-coil self-assembly repressed necroptosis, but it did not prevent RIP3-induced dimerization of the MLKL kinase-like domain. So that, MLKL activation is a sequential process, which begins with kinase-like domain dimerization, and followed by internal coiled-coil region self-assembly to form a proper MLKL oligomer. Besides human MLKL, structural and functional analysis showed the kinase-like domain dimerization was conserved among mammalian species, suggesting it is a general step of the RIP3-induced MLKL activation process. MLKL protein is compromised a N-terminal four-helix bundle (4HB) region taking the charge of membrane disruption, the direct cause of cell necrosis, an internal coiled-coil (CC) region, and a C-terminal kinase-like domain (KLD)6. Usually, necroptosis signaling is mediated by two specific kinases, receptor-interacting protein kinase-1 (RIP1) and RIP3, which are sequentially activated by various extracellular cytokines such as the tumor necrosis factor (TNF)11. While the activation of MLKL is mainly regulated by RIP3-mediated phosphorylation at threonine 357 and serine 358 of human MLKL (or serine 345 of mouse MLKL) in the activation loop of the kinase-like domain12. Since the kinase-like domain of MLKL is catalytically defective, the phosphorylation on the activation loop does not boost the kinase-activity but relieves the auto-inhibitory monomer state of MLKL to promote its oligomerization through the coiled-coil domain-mediated self-assembly14. The oligomerized MLKL proteins translocate to plasma membranes, where the peptides corresponding to the four-helix bundle region insert into the membrane and form channels or pores, disrupting plasma membrane integrity and causing cell necrosis eventually17. Besides phosphorylation mediated by RIP3, other modifications on the MLKL kinase-like domain were reported to regulate MLKL function. One is Tyr376 of human MLKL, which is phosphorylated by membrane-associated TAM kinases to promote further oligomerization of membrane-binding MLKL and facilitate necroptosis execution18. Another is serine 441 of mouse MLKL. This phosphorylation site is mediated by unknown kinases and function to promote myelin breakdown in Schwann cells, a MLKL-dependent but RIP3-independent process in axon regeneration19. Therefore, the kinase-like domain of MLKL is considered as integrator for upstream signals to regulated MLKL activation.Mixed Lineage Kinase domain-like protein (MLKL) is the executioner of necroptosis, a type of programmed cell necrosis. The MLKL caused necrosis participates in the serval physiological events including immune response against pathogens, aging-associated deterioration of the male reproductive system, and satellite cells (muscle stem cells) proliferation in myofiber regeneration5. In this structure, the internal coiled-coil region of MLKL is restricted by both the N-terminal four-helix bundle and C-terminal kinase-like domain. A series of electrostatic interactions including R34-E135, R30-D139, and K26-E142 are present between residues in helix α2 of the four-helix bundle and helix α5 of the coil-coiled region in this structure5. These interactions are also present in the solution structure of the N-terminal part of human MLKL (PDB code: 2MSV)20. Mutation disrupting these interaction such as D139V in mouse MLKL and K26E/R30E in human MLKL could induce MLKL activation which elevates the in vitro liposome leakage efficiency of the MLKL proteins and cause necroptosis bypass the RIP3 regulation21. It suggested the four-helix bundle is directly inhibited by the monomeric coiled-coil region. Besides these mutations on MLKL four-helix bundle and coiled-coil region, there are pseudoactive sites on MLKL kinase-like domain including phosphomimic S345D mutation in mouse MLKL (or T357E/S358D mutations in human MLKL) and mutations disrupting an unusual salt-bridge K219–Q343 present in mouse MLKL structure. Both of the phosphomimic mutants and mutants changing the sites of K219/Q343 in mouse MLKL (or conserved sites K230/Q356 in human MLKL) induce MLKL oligomerization and activation which causes RIP3-independent necroptosis18. While mutations on the coiled-coil region of MLKL, disrupting their self-interaction block RIP3-induced MLKL oligomerization and necroptosis13. Therefore, MLKL activation by the upstream signal is initiated by changing the kinase-like domain, which induces oligomerization of the coiled-coil region. But the how the monomer to oligomer transition of the coiled-coil region controlled by kinase-like domain is still uncovered. Here, we report the phosphorylation by RIP3 can induce the dimerization of the kinase-like domain of MLKL, which is the critical step to induce the following oligomerization and activation of full-length proteins. The crystal structure reveals two binding interfaces in MLKL kinase-like domain dimer. The mutations to disrupt interactions of both interfaces could prevent MLKL dependent necroptosis efficiently. Besides that, we proposed sequential model of MLKL processing in necroptosis execution, and the function of MLKL kinase-like domain dimerization was to initiated oligomerization of MLKL internal coiled-coil region. It is confirmed by the mutations disrupting internal coiled-coil self-assembly, which repressed MLKL-dependent necroptosis downstream of RIP3-mediated MLKL KLD dimerization. More importantly, inconsistent with the previous species-divergent model of MLKL activation, the structural and functional analysis indicated kinase-like domain dimerization was the general step to commit RIP3-induced MLKL activation process among mammalian species.In normal conditions, the kinase-like domain maintain the auto-inhibitory state of MLKL, which is revealed by the crystal structure of the full-length mouse MLKL (PDB code: 4BTF)14. z-VAD-fmk (cat# 1140) is bought from BioVision. Antibodies used for western blots were purchased from Abcam , MBL , Sigma-Aldrich ; Cell Signaling Technology .Recombinant TNF was purified in our lab. Necrostatin-1 (cat# HY-15760) is bought from MedChemExpress. Smac mimetic and ecrosulfonamide (NSA) compound was used as described previouslyStandard PCR and cloning methods were used to clone the full-length or mutated cDNAs of MLKL into the lentiviral vector pCDH-CMV-MCS-EF1-copGFP (Addgene). Wild-type or mutated cDNAs of the full-length and kinase-like domain of MLKL were cloned into pFastBac-I vector (Life Technologies) for baculovirus expression systems or pCMV-N-flag vector(Clontech) for expression in mammalian cells. All plasmids were verified by standard DNA sequencing.2 and tested to be mycoplasma-negative by the standard PCR method. HT-29(MLKL-KO) cells were generated by using the CRISPR/Cas9-genome editing strategy and confirmed by PCR and western blot. The Cas9-target sites are as follows: human MLKL . The stable cell lines were established by the lentiviral expression system. HEK293T cells were transfected with lentiviral vectors (pCDH-CMV-MCS-EF1-copGFP) and virus packing plasmids  by using EZ transfection reagents . The virus-containing medium was harvested 48 hr later and added to HT-29 MLKL KO cells as indicated, with 10 μg/ml polybrene.HT-29 human colon cancer cells, obtained from the cell bank of CAS (Shanghai). were cultured in Dulbecco’s modified Eagle’s medium/HIGH with L-glutamine, without sodium pyruvate . All media were supplemented with 10% FBS  and 100 units/ml penicillin/streptomycin . All the cells were cultured at 37 °C with 5% COCrosslinker disuccinimidyl glutarate  was freshly made by dissolving in DMSO as a 20 mM stock solution. For proteins’ crosslinking, wild-type or mutant forms of MLKL recombinant kinase-like domain proteins were incubated with 500 μM DSG for 30 min at room temperature, along with DMSO as a negative control. The reaction was stopped by the addition of 100 mM Tris-HCl (pH 7.5) and incubated for 15 min at room temperature according to the user’s instruction. Then, samples were separated by SDS-PAGE and analyzed by Coomassie blue staining. For crosslinking in lysates, HEK293T or HEK293T-RIP3 cells plated in a 60 mm dish were transfected with 6 μg of MLKL expression vectors for 24 h (HEK293T) or for 12 h then treated with T/S/Z for 8 h (HEK293T-RIP3). The cells were harvested, and the whole-cell lysates were incubated with or without 100 μM compounds on ice for 4 h, then mixed with DMSO (as a control) or 500 μM crosslinker DSG for 30 min. After stopping the reaction, samples were separated by SDS-PAGE and analyzed by western blotting.The cDNAs encoding wild-type or mutant forms of the truncated kinase-like domain  MLKL were subcloned into the expression vector pFastBac-I  with an N-terminal GST tag followed by sequence of HRV3C Protease  cleavage site. The plasmid was transformed into DH10Bac E. coli cells . The recombinant viral DNA bacmid was purified according to the Bac-To-Bac Baculovirus Expression procedure and confirmed by PCR amplification analysis. The proteins were expressed in Sf9 insect cells and were purified with Glutathione Sepharose 4B  in wash buffer . The GST tag was removed by on-column digestion with homemade HRV3C Protease for overnight at 4 °C. The proteins were collected and concentrated for further purification by Superdex 75 or Superdex 200 gel filtration chromatography . Protein was eluted in storage buffer (20 mM Hepes-NaOH pH 7.5 and100 mM NaCl) for storage.The cDNAs encoding wild-type or mutant forms of MLKL (126 ~181 aa) subcloned into the expression vector PET-SUMO .The plasmid was transformed into BL21(DE3) E. coli cells . The proteins were purified with NTA resin , then further purification by Superdex 200 gel filtration chromatography . Protein was eluted in storage buffer (20 mM Hepes-NaOH pH 7.5 and100 mM NaCl) for storage.Blue Native-PAGE was using Tris-glycine native gel. Recombinant wild-type or mutant forms of MLKL-KLD proteins (2 mg/mL) were mixed with Native-PAGE loading buffer (containing 0.1% Coomassie brilliant blue G250). Analytes were applied to 10% Tris-glycine native gel in blue cathode buffer with 0.02% Coomassie brilliant blue G250, and electrophoresis was performed at room temperature, 115 V, 40 mA. The gels were stained with Coomassie Brilliant Blue G250.The cells were seeded into 96-well plates and allowed to grow for about 12 h at the 5000 cells/well. Cell survival assay was performed using the CellTiter-Glo Luminescent Cell Viability Assay kit  according to the manufacturer’s instructions. Luminescence was recorded with a SpectraMax i3 multimode plate reader from Molecular Devices.Cells cultured on 60 mm dishes were collected and washed once with DPBS. The harvested cells were resuspended in lysis buffer containing 20 mM Hepes-NaOH pH 7.5, 150 mM NaCl, 1% Triton X-100, 10% glycerol, and complete protease inhibitor (Roche) for 30 min on ice. Then, cell lysates were centrifuged at 15,000 g at 4 °C for 20 min. The soluble fraction was collected and boiled with loading buffer. The samples were subjected to SDS-PAGE and transferred onto nitrocellulose membrane. The membrane was blocked with 5% skim milk for 1 h at room temperature and incubated with indicated antibody. The membrane was visualized using Amersham Imager 680 with the ECL reagent .22. Data collection statistics are summarized in Table S6) as the search model23. Phenix was used to build and refine the model24.Recombinant kinase-like domain mutated MLKL proteins were concentrated to 5 mg/ml and subjected to robotic crystal trials. All crystals were grown at 20 °C using the vapor-diffusion on plates method. Crystals of human MLKL kinase-like domain  appeared in a well buffer containing 0.1 M HEPES pH 7.5, 14% w/v PEG6000, and 5% EG. Crystals of human MLKL kinase-like domain  appeared in a well buffer containing 0.1 M HEPES pH 7.5, 5% v/v (+/−)−2-Methyl-2,4-pentanediol, and 10% w/v Polyethylene glycol 10000. Native datasets were collected at the Shanghai Synchrotron Radiation Facility (SSRF) beamline BL17U1 and BL19U1 were processed with HKL2000 packagewww.ebi.ac.uk/pdbe/pisa/)25.Contact areas and binding energies of the dimer interface of MLKL kinase-like domain were analyzed using the protein interfaces, surfaces, and assemblies (PISA) web server . representative of duplicate or triplicate wells with error bars represent mean ± SD, and similar results were obtained from at least three independent experiments.The oligomerization of full-length MLKL protein is a critical step of MLKL activation. It is triggered by RIP3-mediated phosphorylation on their kinase-like domain and mediated by self-assembly of the MLKL coiled-coil region. Before RIP3-mediated phosphorylation, the cellular endogenous MLKL proteins remain monomeric. It is still not resolved how phosphorylation in the kinase-like domain of MLKL initiates self-assembly of the coiled-coil domain. To elucidate the mystery of phosphorylation induced kinase-like domain activation, protein behaviors of the recombinant kinase-like domain of wild-type, phosphomimic (T357E/S358D) mutant and phosphor-sites’ mutant (T357A/S358A) of human MLKL proteins were compared by gel filtration chromatography. A peak between the 67 and 14 kDa molecular size markers were observed for wild-type proteins Fig. . All thrTo reveal the molecular mechanism of MLKL kinase-like domain dimerization, we crystallized the recombinant the phosphor-sites’ mutant (T357A/S358A) and phosphomimic mutant (T357E/S358D) forms of MLKL kinase-like domain proteins Table S. Interes2, and 35 residues involved in dimer assembly. The dimer interface was composed by ten hydrogen bonds and eight salt bridges and some of hydrophobic interactions25. In the center of dimer interface, Glu258 of one MLKL molecule ionically interacted with Arg264 of the other molecule; Pro260 of one MLKL and Phe266 of the other molecular stack against each other by CH/π interaction. At the side of dimer interface, Arg224 of one MLKL molecular interacted with Glu460 of the other molecular by ionic bond; and the phenyl rings from Tyr318 of one MLKL molecular hydrophobic interacted with hydrocarbon chain of Arg224 and backbone of His223 of the other molecular  alone could form oligomer in vitro, while the mutations on the four critical residues to alanine  prevented their oligomerization in gel-filtration analysis  mutations on KLD dimer interfaces inhibited TNF-induced necroptosis. To avoid the possibility that these mutations affect the proper folding of MLKL to disrupt MLKL function, we checked the phosphorylation of MLKL(AAAGAA) mutant by RIP3. It showed that the p-MLKL signals were comparable between wild-type and a mutant form of MLKL, suggesting that the cellular MLKL mutant was folded well to be the substrate of RIP3 as wild-type MLKL Fig. . The dimsis Fig.  and S2A.sis Fig. . It showsis Fig. , they besis Fig. . This co27. Therefore, the presumption of divergent activation mechanisms among these mammalian MLKL orthologues was raised26. The species-divergent model may suggest the KLD dimerization is conserved step of MLKL activation in human, rat, and horse, but not in mouse. Indeed, similar MLKL dimer could be found in the crystal lattice of rat and horse crystal structure , rat (PDB code: 6VBZ), and horse (PDB code: 6VC0) MLKL kinase-like domain presented a closed-state conformation, charactered by a conserved slat-bridge salt bridge  which is equivalent of the K72–E91 interaction in protein kinase A(PKA). While the mouse MLKL kinase-like domain presents a more open-state conformation from PDB 4BTF), charactered by a unique salt bridge of K219–Q343ure Fig. . But inture Fig. . AlthougTF, charaure Fig. . It confPhosphorylation of MLKL by RIP3 is a hallmark of necroptosis, which causes MLKL activation and initiates MLKL dependent necroptosis execution. Here, we found MLKL kinase-like domain dimerization is a critical step of MLKL activation and necroptosis, which is conserved in mammalian species. MLKL kinase-like domain dimerization interrupts the monomeric conformation and induces oligomerization of full-length MLKL, suggesting a serial activation model of MLKL mediated necroptosis execution Fig. : Step 1,27. But our results indicated these differences may not affected MLKL activation process after RIP3-phosphorylation. All the mammalian MLKL uses kinase-like domain dimerization as the general step next to RIP3-phoshorylation which is critical to commit MLKL activation process. Besides that, the MLKL KLD dimer is only found in the crystal lattice of the mouse RIP3-MLKL complex (PDB code: 4M69), but not in crystal lattices of mouse MLKL full-length and KLD structure (PDB code: 4BTF and 4M68). It suggested the MLKL KLD conformations in 4BTF and 4M68 is in different state from MLKL in the RIP3-MLKL complex. The mouse MLKL KLD in RIP3-MLKL complex or the reported human, rat, and horse MLKL KLD structure are probably in an active-state. While the MLKL KLD conformation in 4BTF and 4M68 structure may be in a closed or inactive state. Interestingly, the 4BTF and 4M68 structure have a unique K213–Q343 slat-bridge which stabilized their closed-state conformations. Mutants changing the sites of K219/Q343 in mouse MLKL (or conserved sites K230/Q356 in human MLKL) induce MLKL activation which causes RIP3-independent necroptosis18. It indicates the human, rat, or horse MLKL may also have similar closed-state conformation hallmarked with the K230–Q356 salt-bridge in human or other conserved salt-bridges in other species, which will be captured in the future by trails of more crystallization conditions. Indeed, two new monobody-stabilized human KLD structures have been recently reported which showed different conformations28. The monobody-27 binding MLKL KLD (PDB code: 7JW7) shows active-state conformation, which contains the functional dimer interface in the crystal lattices. While the monobody-33 binding MLKL KLD (PDB code: 7JXU) shows closed-state conformation hallmarked with the K230–Q356 salt-bridge. And the functional dimer interface we identified was not in the monobody-33 binding MLKL crystal lattices. These clearly indicates the previous MLKL specie-divergent activation model which is based on the structural differences of mouse MLKL KLD from MLKL of other species need to be optimized and amended26. Importantly, in all active-state MLKL KLD structures, residues around the RIP3-target residues S345 in mouse (or T357/S358 in human) are disordered. While the mouse K219–Q343 (or human K230–Q356) salt-bridge stabilized KLD structure  the nearby residues around RIP3-target mouse S345 (or human T357/S358) form helix conformations. It may suggest the phosphorylation on mouse S345 (or human T357/S358) will disturb the helix conformation to a disordered state which breaks mouse K219–Q343 (or human K230–Q356) salt-bridge (indicated by the close proximity of mouse Q343 or human Q356 with the mouse S345 or human T357/S358 in MLKL) to drive the transition of MLKL KLD proteins from inactive-state to active dimers.Divergent activation mechanisms among mammalian MLKL orthologues have been raised based on the structural differences between human and mouse MLKL kinase-like domain of human andSupplemental Figure 1~3 with legendsTable-S1


Neocallimastix patriciarum (GenBank accession no. KP691331), submodule C2 from hyperthermophilic CBM9_1-2 was inserted into the N- and/or C-terminal regions of the CDBFV protein  by genetic engineering. CDBFV and the hybrid proteins were successfully expressed in Escherichia coli BL21 (DE3). Enzymatic property analysis indicates that the C2 submodule had a significant effect on enhancing the thermostability of the CDBFV. At the optimal temperature (60.0 °C), the half-lives of the three chimeras C2-CDBFV, CDBFV-C2, and C2-CDBFV-C2 are 1.5 times (37.5 min), 4.9 times (122.2 min), and 3.8 times (93.1 min) longer than that of wild-type CDBFV (24.8 min), respectively. More importantly, structural analysis and molecular dynamics (MD) simulation revealed that the improved thermal stability of the chimera CDBFV-C2 was on account of the formation of four relatively stable additional hydrogen bonds , which increased the protein structure’s stability. The thermostability characteristics of CDBFV-C2 make it a viable enzyme for industrial applications.Xylanases have been applied in many industrial fields. To improve the activity and thermostability of the xylanase CDBFV from  Carbohydrate degradation involves a series of hydrolases, especially xylanase EC 3.2.1.8) [.8 [1]. XMost xylanases from different species are categorized into glycoside hydrolase (GH) families 10 and 11, with the remainder belonging to families 5, 8, 30, and 43 based on the sequence similarities in the catalytic domain . GH11 xyhttp://www.cazy.org, accessed on 10 October 2021) [Thermotoga maritima was fused with Xyn2 of Trichoderma reesei, resulting in increased thermostability [Thermotoga neapolitana, the thermostability of Bacillus halodurans xylanase was reduced [Clostridium thermocellum did not affect its thermostability [C. thermocellum decreased its thermostability, but CBM36 deletion from Xyn11 of Caldicellulosiruptor sp. F32 improved its thermostability [T. maritima GH10 xylanase A to the C-terminus of xylanase from Aspergillus niger GH11, the thermal stability of the Xyn-CBM9_1-2 chimera is slightly reduced [A. niger xylanase [N. patriciarum but lacks a CBM in the wild xylanase sequence [Carbohydrate-binding modules (CBMs) are divided into different families based on their sequence similarities, and to date, 88 families are listed in the online database CAZy er 2021) . In additability . With th reduced . The addtability . Howevertability ,15. In t . In addtability ,16. Thistability . CBMs ortability ,16,17. Mxylanase ,14,15,16sequence .T. maritima xylanase (Swiss-prot: Q60037), to the N- and C-termini and both termini of the catalytic domain of CDBFV on the activity, thermostability, substrate-binding properties, and kinetic parameters of the variants are described in this study. In addition, the specific influence of the C2 submodule on the structure of CDBFV is explained through structural analysis and molecular dynamics simulation.The effects of adding the C2 submodule from hyperthermophilic CBM9_1-2, according to the database annotation for CDBFV-1 , C2L (including submodule C2 and linker), LC2 (including linker and submodule C2) a, and CDThe degradation activities of four xylanases  on Corncob xylan, Birch xylan, Bagasse xylan, Avicel, and CMC-Na were determined at pH 5.5 and 60.0 °C. The results showed that the four xylanases had the best degradation activity on Corncob xylan and Birch xylan, followed by bagasse xylan, and almost did not degrade Avicel and CMC-Na . On diffKM values were slightly lower  of the four xylanases was determined over a wide temperature range (10.0–80.0 °C). The enzymes were most active at 60.0 °C, as shown in t1/2) of four xylanases at different temperatures were compared . At 65.0 °C, the t1/2 of the three chimeras C2-CDBFV, CDBFV-C2, and C2-CDBFV-C2 was 13.7, 44.2, and 31.6 min; it was 1.6, 5.1, and 3.6 times longer than that of CDBFV (8.8 min). At 70.0 °C, the t1/2 of the three chimeras C2-CDBFV, CDBFV-C2, and C2-CDBFV-C2 was 1.5 times (8.1 min), 4.8 times (25.3 min), and 3.6 times (19.2 min) longer than that of wild-type CDBFV (5.3 min), respectively  and xylanase 10A of T. maritima (PDB ID: 1I8U_A), respectively. The two chimeric xylanases were predicted from the zero-folded protein structure by trRosetta. Five structural models were obtained for each chimera. A modeler was used to fill in the atoms that may be missing in the structure of each obtained model. SAVES V6.0 was used to evaluate all 3D models, and Ramachandran plots of these models were obtained  and C-terminal (CDBFV-C2) connections of the C2 submodule, the trRosetta server was employed based on the energy-minimized structure algorithm and 15,000 amino acid multiple sequence alignment modeling. Among them, the wild-type CDBFV and C2 submodule exhibit the highest structural consistency with xylanase from obtained . In the obtained . Howeverobtained a.To further explore the specific interaction of the different connection directions of submodule C2 on the protein structure of CDBFV, we explored the protein structure of the hydrogen bond network between the submodule C2, linker, and CDBFV. First, we analyzed the hydrogen bond interaction between the three parts of the chimeric xylanase C2-CDBFV, and we found that there is no interaction between the submodule C2 and CDBFV. Only two additional hydrogen bonds were formed between the linker and CDBFV, namely, E240-K267 and V244-S260, respectively . Then, wα atom, which was calculated using MD simulation data for the last 30.0 ns of the initial structure  simulation was performed on the chimeric xylanase CDBFV-C2 and its components. Generally speaking, the root means square deviation (RMSD) values of the chimeric CDBFV-C2 and its components—CDBFV, Linker, and C2 substructures—are all less than 0.7 nm a. Addingtructure b. Generatructure b. The reTo gain insight into the improved thermostability of the chimeric xylanase CDBFV-C2, the stability of eight additional hydrogen bonds was monitored during the simulation c. DuringThe development of thermostable xylanase is critical for meeting practical industrial demands . To obtaA. niger xylanase could increase its thermal stability by about four times compared with wild-type xylanase, which is consistent with our conclusions [First, the C2 submodule, in particular, improved the thermostability of xylanase. In the previous study, this effect was investigated by dividing the CBM9_1-2 module into two smaller parts, C1 and C2, which were transplanted into Xyn to create the chimeras Xyn-C1 and Xyn-C2. The Xyn-C2 thermal inactivation half-life 69.3 min) was four or five times longer than that of Xyn (17.6 min), Xyn-C1 13.4 min), and the original chimera containing CBM9_1-2 (13.8 min) ,20,21. C min was clusions ; (2) the.4 min, aclusions ,14,16. TSecond, adding CBM to wild enzymes can be performed via various methods. The possibilities include 1) adding CBM to the N-terminus of the wild enzyme ,12,22;  adding C. The CDBA. niger xylanase and the module C2 submodule [A. niger xylanase can improve the binding efficiency of insoluble substrates [A. niger xylanase is only 38.0%, and their structures and properties are very different, leading to differences between the two results [Third, another exciting aspect of this study is that fusing the thermostable module C2 submodule to the N-terminus, C-terminus, and both termini of CDBFV increased the activity of CDBFV. Compared with wild-type CDBFV, the specific activities of the chimeric xylanase added to the N-, C-, and both termini of CDBFV increased by 17.8, 18.7, and 27.9%, respectively . The samubmodule . This alubmodule . The enz results ,26; (2)  results .Finally, the linker peptide provides the necessary space for each domain to form an active conformation, and the appropriate linker is an important factor that affects the folding of the domains ,28,29,30N. patriciarum in GenBank (GenBank accession no. KP691331), which contains the 675-base-pair CDBFV sequence, was inserted into pMD19-T [T. maritima GH10 xylanase A (GenBank accession no. GQ169711) were synthesized into pMD19-T (pMD19-T-LC2L) to form a template for amplification [® HD Cloning Kit, Taq DNA polymerase, restriction endonucleases , dNTP, IPTG, DNA marker, and protein marker were purchased from Takara Inc. . The plasmid miniprep kit and DNA gel extraction kit were obtained from Omega . A Bradford protein assay kit, pET-28a (+) expression vector, E. coli DH5α for cloning, and E. coli BL21 (DE3) cells for expression were purchased from TransGen . All other chemicals were of analytical grade and commercially available.The CDBFV gene from  pMD19-T . The CBMEcoR I and Not I. This fusion was performed using an In-Fusion® HD Cloning Kit, as shown in E. coli DH5α competent cells were then transformed with the recombinant plasmids pET-28a-CDBFV, pET-28a-CDBFV-C2, pET-28a-C2-CDBFV, and pET-28a-C2-CDBFV-C2. Recombinant DNA from the clones, after confirmation by sequencing, was used to transform E. coli BL21.To obtain the chimeras, first, a variant with a C2 submodule fused at both termini of CDBFV was constructed. DNA fragments CDBFV-1, C2-linker (C2L), and linker-C2 (LC2) were amplified separately using 0.3 μL pMD19-T-CDBFV or pMD19-T-LC2L as the template (50.0 ng/μL) and P1/P2, P3/P4, or P5/P6  as prime600 nm reached ~0.7, and xylanases were induced by adding IPTG to a final concentration of 0.5 mM. After further incubation for 15.0 h at 20.0 °C, supernatants were harvested after the cells were disrupted by high-pressure cell breakage  and centrifugation at 13,000 rpm/min for 20.0 min at 4.0 °C. After removing cell debris, both the N-terminal and C-terminal His-tagged enzymes containing the supernatant were purified by affinity chromatography using a Ni-NTA agarose column. Afterward, the purified xylanases were detected using 12.0% polyacrylamide SDS–PAGE analysis, and the enzyme concentration was measured by a Bradford protein assay kit. The gene fragment size and protein molecular mass of the chimeras were calculated by DNAMAN v6 [Transformed cells were grown in LB medium at 37.0 °C until the ODNAMAN v6 ,18.Xylanases’ standard activity was measured using the 3,5-dinitrosalicylic acid (DNS) method [KM and Vmax values for purified xylanases were determined using 0.0%-1.0% Corncob xylan as the substrate in pH 5.5 citrate phosphate buffer at 60.0 °C. The data were fit based on the classic Michaelis–Menton function.The enzymatic activity of CDBFV was assayed in parallel with the variants C2-CDBFV, CDBFV-C2, and C2-CDBFV-C2 for assessment of properties. The optimal pH for the purified xylanases activity was determined with buffer pH values from 3.0 to 9.0 at 37.0 °C. The enzymatic stabilities in various pH ranges were judged by measuring the residual enzymatic activities after incubating the enzymatic solution at different buffer pH values from 3.0 to 11.0 at 37.0 °C for 60.0 min. The pH ranges of the buffers were as follows: citrate phosphate buffer (3.0–8.0), Tris-HCl (7.0–9.0), and glycine-NaOH (9.0–12.0). The optimal temperature of purified xylanases was measured within the range from 10.0 to 80.0 °C at pH 5.5. Thermostability was determined at pH 5.5 and 60.0 °C after preincubating the enzyme for different times at 60.0, 65.0, or 70.0 °C. Xylanase-specific activity was assayed on sodium carboxymethyl cellulose (CMC-Na), Avicel, Beechwood xylan, Bagasse xylan, and Corncob xylan . The binding ability of the insoluble substrates was analyzed by measuring the protein binding rate in the reaction system after the enzyme solution was reacted at 60.0 °C for 2.0 h . The KM N. patriciarum (PDB ID: 3WP4) [T. maritima (PDB ID: 1I8U_A) [https://yanglab.nankai.edu.cn/trRosetta/, accessed on 14 September 2021) according to the methods described previously [The crystal structures of CDBFV from D: 3WP4)  and C2 f 1I8U_A)  were useeviously ,34,35. Teviously ,34,35. Teviously ,37.The structure of the chimera xylanase CDBFV-C2 was built by trRosetta server ,34,35. THydrogen bonds between amino acid residues in the simulation system were analyzed by using g_hbond in the GROMACS suite. Geometrical criteria, which include donor–acceptor distance (≤0.4 nm) and hydrogen-donor–acceptor angle (≤30.0°), are used to calculate hydrogen bond. For each time frame, if both the donor–acceptor distance and the hydrogen-donor–acceptor angle satisfy the criteria, the number of hydrogen bonds will be counted as 1.0, and 0.0 otherwise. The number of hydrogen bonds was calculated based on the whole 30.0 ns simulation  in the system, and the error bar represents one standard error based on the averaged number of hydrogen bonds every 100.0 ps in each system.N. patriciarum and the N- and C-termini (both termini of which fuse a C2 submodule from a hyperthermophilic CBM9_1-2) can enhance thermostability. Compared with wild-type xylanase, the half-lives of the three chimeras increased by about 1.5, 5.0, and 3.7 times, respectively. Structural analysis showed that the thermal stability of the chimera CDBFV-C2 with the best thermal stability was significantly improved by the formation of four relatively stable additional hydrogen bonds , which is beneficial to increasing the stability of the CDBFV-C2 protein structure, thereby significantly improving its thermal stability.This research shows that the xylanase from


Dear Editor,NCF1 gene with early-onset interferonopathy. Whilst heterozygosity for the rare rs201802880 p.Arg90His variant of NCF1 had been associated previously with susceptibility to systemic lupus erythematosus (SLE), rheumatoid arthritis (RA) and Sjögren’s syndrome in adult patients [NCF1, altering an arginine to histidine in a PX domain of the NCF1 protein, in humans leads to reduction-of-function of NADPH oxidase [We read with great interest in Pediatric Rheumatology the article by Schnappauf et al.  referredpatients . The rs2 oxidase .phox PX Domain has been shown in the past to be the direct involvement in recognition of the polar heads of phosphoinositides [phox domain with the phosphate groups (shift from 2.4 to 6 A shown with dashed lines) but also weakens the positive charge distribution on the molecular surface. In addition, the shorter His side chain creates an empty volume between the histidine imidazole group and the phosphate at a distance of 6 A causing either further conformational changes or rehydration of the molecular surface both leading to loss of the p47phox PX Domain to phospoinositide interaction. This is in agreement with the multidiscipline studies by Karathanassis et al. [phox PX Domain decrease membrane affinities, resulting in longer membrane residence time due to disruption of their interaction with the cognate phospholipid ligand. The loss of the domain’s capacity to bind PIP2 shows significant loss of translocation to the plasma membrane [Prompted by the findings of Schnappauf et al.  and the ositides . By usinositides ) that exs et al.  showing membrane .Fig. 1Rphox PX Domain. We conclude that this SNP modifies the function of the p47phox PX cytosolic subunit of neutrophil NADPH oxidase leading to affinity reduction to PtdInsP2 caused by the loss of specific phosphoinositide headgroup interactions and affecting the p47phox translocation to the plasma membrane. This information would help to further interpret the findings of Schnappauf et al. [To our knowledge, this is the first study to evaluate the structural significance of the rs201802880 SNP causing the Arg90His mutation on the p47