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Conventional multiobjective optimization algorithms (MOEAs) with or without preferences are successful in solving multi- and many-objective optimization problems. However, a strong hypothesis underlying their performance is that MOEAs are able to find a representative solution set to cover the entire Pareto-optimal front (PF) and decision makers are able to conveniently and precisely articulate their preference, which is not always easy to fulfill in practice. Accordingly, it is suggested that representative solutions in the naturally interesting regions of the PF rather than the whole PF should be targeted. A large body of research has been proposed to search or identify the knees or knee regions over the past decades. Therefore, this article aims to provide a comprehensive survey of the research on knee-oriented optimization. We start with a discussion of the importance and basic concepts of the knees, followed by a summary of knee-oriented benchmarks and indicators. After that, knee-oriented frameworks and techniques, and real-world applications are presented. Finally, potential challenges are pointed out and a few promising future lines of research are suggested. The survey offers a new perspective to develop MOEAs for solving multi- and many-objective optimization problems. [ABSTRACT FROM AUTHOR]

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the $\ell _{0}$ -norm usually achieve better performance than the convex case (e.g., $\ell _{1}$ -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Łojasiewica (KŁ) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. [ABSTRACT FROM AUTHOR]

The critical step of learning the robust regression model from high-dimensional visual data is how to characterize the error term. The existing methods mainly employ the nuclear norm to describe the error term, which are robust against structure noises (e.g., illumination changes and occlusions). Although the nuclear norm can describe the structure property of the error term, global distribution information is ignored in most of these methods. It is known that optimal transport (OT) is a robust distribution metric scheme due to that it can handle correspondences between different elements in the two distributions. Leveraging this property, this article presents a novel robust regression scheme by integrating OT with convex regularization. The OT-based regression with $L_{2} $ norm regularization (OTR) is first proposed to perform image classification. The alternating direction method of multipliers is developed to handle the model. To further address the occlusion problem in image classification, the extended OTR (EOTR) model is then presented by integrating the nuclear norm error term with an OTR model. In addition, we apply the alternating direction method of multipliers with Gaussian back substitution to solve EOTR and also provide the complexity and convergence analysis of our algorithms. Experiments were conducted on five benchmark datasets, including illumination changes and various occlusions. The experimental results demonstrate the performance of our robust regression model on biometric image classification against several state-of-the-art regression-based classification methods. [ABSTRACT FROM AUTHOR]

In recent years, low-rank matrix recovery problems have attracted much attention in computer vision and machine learning. The corresponding rank minimization problems are both combinational and NP-hard in general, which are mainly solved by both nuclear norm and Schatten-p ($0 < {p} < 1$) norm based optimization algorithms. However, inspired by weighted nuclear norm and Schatten-p norm as the relaxations of rank function, the main merits of this work firstly provide a modified Schatten-p norm in the affine matrix rank minimization problem, denoted as the modified Schatten-p norm minimization (MSpNM). Secondly, its surrogate function is constructed and the equivalence relationship with the MSpNM is further achieved. Thirdly, the iterative singular value thresholding algorithm (ISVTA) is devised to optimize it, and its accelerated version, i.e., AISVTA, is also obtained to reduce the number of iterations through the well-known Nesterov’s acceleration strategy. Most importantly, the convergence guarantees and their relationship with objective function, stationary point and variable sequence generated by the proposed algorithms are established under some specific assumptions, e.g., Kurdyka-Łojasiewicz (K) property. Finally, numerical experiments demonstrate the effectiveness of the proposed algorithms in the matrix completion problem for image inpainting and recommender systems. It should be noted that the accelerated algorithm has a much faster convergence speed and a very close recovery precision when comparing with the proposed non-accelerated one. [ABSTRACT FROM AUTHOR]

Recently, nuclear norm-based low rank representation (LRR) methods have been popular in several applications, such as subspace segmentation. However, there exist two limitations: one is that nuclear norm as the relaxation of rank function will lead to the suboptimal solution since nuclear norm-based minimization subproblem tends to the over-relaxations of singular value elements and treats each of them equally; the other is that solving LRR problems may cause more time consumption due to involving singular value decomposition of the large scale matrix at each iteration. To overcome both disadvantages, this paper mainly considers two tractable variants of LRR: one is Schatten- ${p}$ norm minimization-based LRR (i.e., ${S}_{p}$ NM_LRR) and the other is Schatten- ${p}$ norm factorization-based LRR (i.e., ${S}_{p}$ NF_LRR) for ${p=1}$ , 2/3 and 1/2. By introducing two or more auxiliary variables in the constraints, the alternating direction method of multiplier (ADMM) with multiple updating variables can be devised to solve these variants of LRR. Furthermore, both computational complexity and convergence property are given to evaluate nonconvex multiblocks ADMM algorithms. Several experiments finally validate the efficacy and efficiency of our methods on both synthetic data and real world data. [ABSTRACT FROM AUTHOR]

For face recognition, sparse coding based classification methods have demonstrated the inspiring attraction in dealing with the pixel-level based sparse or gaussian noise, and nuclear norm based matrix regression (NMR) methods have shown the robustness in handling with the image-level based structural noise (e.g., occlusions, illuminations). Such regression methods have two limitations: one is that both ignore the label information of training samples and the similarity relationship between the training samples and the testing ones, the other is that nuclear norm as the rank relaxation can make the obtained solution deviate from the original matrix since its over-relaxations to the larger singular values. To overcome both limitations, this paper presents nonconvex relaxation based matrix regression (NRMR) methods: one is called locality-constrained group sparsity regularized nonconvex matrix gamma norm regression model for the s tructural noise (NRMRS), and the other extends it to the locality-constrained group sparsity regularized matrix gamma and minimax concave plus (MCP) regression model for the m ixed noise (NRMRM), i.e., structural noise plus sparse noise. Two variants of inexact augmented Lagrange multiplier (IALM) algorithms including nonconvex IALM (NC-IALM) and majorization–minization IALM (MM-IALM) are devised to solve the proposed models, respectively. Finally, experiments on Extended Yale B, AR and CUHKFS databases can show the superiority of our methods to face recognition with structural noise and mixed noise. [ABSTRACT FROM AUTHOR]

Face recognition (FR) via regression analysis-based classification has been widely studied in the past several years. Most existing regression analysis methods characterize the pixelwise representation error via l1 -norm or l2 -norm, which overlook the 2D structure of the error image. Recently, the nuclear norm-based matrix regression model is proposed to characterize low-rank structure of the error image. However, the nuclear norm cannot accurately describe the low-rank structural noise when the incoherence assumptions on the singular values does not hold, since it overpenalizes several much larger singular values. To address this problem, this paper presents the robust nuclear norm to characterize the structural error image and then extends it to deal with the mixed noise. The majorization–minimization (MM) method is applied to derive a iterative scheme for minimization of the robust nuclear norm optimization problem. Then, an efficiently alternating direction method of multipliers (ADMM) method is used to solve the proposed models. We use weighted nuclear norm as classification criterion to obtain the final recognition results. Experiments on several public face databases demonstrate the effectiveness of our models in handling with variations of structural noise (occlusion, illumination, and so on) and mixed noise. [ABSTRACT FROM PUBLISHER]

Objectives: Angiopoietin-like protein 2 (ANGPTL2), a recently identified pro-inflammatory cytokine, is mainly secreted from the adipose tissue. This study aimed to explore the role of ANGPTL2 in adipose tissue inflammation and macrophage activation in a mouse model of diabetes. Methodology/Principal Findings: Adenovirus mediated lacZ (Ad-LacZ) or human ANGPTL2 (Ad-ANGPTL2) was delivered via tail vein in diabetic db/db mice. Ad-ANGPTL2 treatment for 2 weeks impaired both glucose tolerance and insulin sensitivity as compared to Ad-LacZ treatment. Ad-ANGPTL2 treatment significantly induced pro-inflammatory gene expression in white adipose tissue. We also isolated stromal vascular fraction from epididymal fat pad and analyzed adipose tissue macrophage and T lymphocyte populations by flow cytometry. Ad-ANGPTL2 treated mice had more adipose tissue macrophages (F4/80+CD11b+) and a larger M1 macrophage subpopulation (F4/80+CD11b+CD11c+). Moreover, Ad-ANGPTL2 treatment increased a CD8-positive T cell population in adipose tissue, which preceded increased macrophage accumulation. Consistent with our in vivo results, recombinant human ANGPTL2 protein treatment increased mRNA levels of pro-inflammatory gene products and production of TNF-α protein in the human macrophage-like cell line THP-1. Furthermore, Ad-ANGPTL2 treatment induced lipid accumulation and increased fatty acid synthesis, lipid metabolism related gene expression in mouse liver. Conclusion: ANGPTL2 treatment promotes macrophage accumulation and activation. These results suggest potential mechanisms for insulin resistance. [ABSTRACT FROM AUTHOR]

To enhance the effectiveness and efficiency of subspace clustering in visual tasks, this work introduces a novel approach that automatically eliminates the optimal mean, which is embedded in the subspace clustering framework of low-rank representation (LRR) methods, along with the computationally factored formulation of Schatten p -norm. By addressing the issues related to meaningful computations involved in some LRR methods and overcoming biased estimation of the low-rank solver, we propose faster nonconvex subspace clustering methods through joint Schatten p -norm factorization with optimal mean (JS p NFOM), forming a unified framework for enhancing performance while reducing time consumption. The proposed approach employs tractable and scalable factor techniques, which effectively address the disadvantages of higher computational complexity, particularly when dealing with large-scale coefficient matrices. The resulting nonconvex minimization problems are reformulated and further iteratively optimized by multivariate weighting algorithms, eliminating the need for singular value decomposition (SVD) computations in the developed iteration procedures. Moreover, each subproblem can be guaranteed to obtain the closed-form solver, respectively. The theoretical analyses of convergence properties and computational complexity further support the applicability of the proposed methods in real-world scenarios. Finally, comprehensive experimental results demonstrate the effectiveness and efficiency of the proposed nonconvex clustering approaches compared to existing state-of-the-art methods on several publicly available databases. The demonstrated improvements highlight the practical significance of our work in subspace clustering tasks for visual data analysis. The source code for the proposed algorithms is publicly accessible at https://github.com/ZhangHengMin/TRANSUFFC.

Decomposing data matrix into low-rank plus additive matrices is a commonly used strategy in pattern recognition and machine learning. This article mainly studies the alternating direction method of multiplier (ADMM) with two dual variables, which is used to optimize the generalized nonconvex nonsmooth low-rank matrix recovery problems. Furthermore, the minimization framework with a feasible optimization procedure is designed along with the theoretical analysis, where the variable sequences generated by the proposed ADMM can be proved to be bounded. Most importantly, it can be concluded from the Bolzano-Weierstrass theorem that there must exist a subsequence converging to a critical point, which satisfies the Karush-Kuhn-Tucher (KKT) conditions. Meanwhile, we further ensure the local and global convergence properties of the generated sequence relying on constructing the potential objective function. Particularly, the detailed convergence analysis would be regarded as one of the core contributions besides the algorithm designs and the model generality. Finally, the numerical simulations and the real-world applications are both provided to verify the consistence of the theoretical results, and we also validate the superiority in performance over several mostly related solvers to the tasks of image inpainting and subspace clustering.

Ceramics, often exhibiting important functional properties like piezoelectricity, superconductivity, and magnetism, are usually mechanically brittle at room temperature and even more brittle at low temperature due to their ionic or covalent bonding nature. The brittleness in their working temperature range (mostly from room down to cryogenic temperatures) has been a limiting factor for the usefulness of these ceramics. In this Letter, we report a surprising "low-temperature toughening" phenomenon in a La-doped CaTiO&#95;{3} perovskite ceramic, where a 2.5× increase of fracture toughness K&#95;{IC} from 1.9 to 4.8  MPa m^{1/2} occurs when cooling from above room temperature (323 K) down to a cryogenic temperature of 123 K, the lowest temperature our experiment can reach. In situ microscopic observations in combination with macroscopic characterizations show that this desired but counterintuitive phenomenon stems from a reentrant strain-glass transition, during which nanosized orthorhombic ferroelastic domains gradually emerge from the existing tetragonal ferroelastic matrix. The temperature stability of this unique microstructure and its stress-induced transition into the macroscopic orthorhombic phase provide a low-temperature toughening mechanism over a wide temperature range and explain the observed phenomenon. Our finding may open a way to design tough ceramics with a wide temperature range and shed light on the nature of reentrant transitions in other ferroic systems.

Background: Vein graft failure remains a common clinical challenge. We applied a systems approach in mouse experiments to discover therapeutic targets for vein graft failure., Methods: Global proteomics and high-dimensional clustering on multiple vein graft tissues were used to identify potential pathogenic mechanisms. The PPARs (peroxisome proliferator-activated receptors) pathway served as an example to substantiate our discovery platform. In vivo mouse experiments with macrophage-targeted PPARα small interfering RNA, or the novel, selective activator pemafibrate demonstrate the role of PPARα in the development and inflammation of vein graft lesions. In vitro experiments further included metabolomic profiling, quantitative polymerase chain reaction, flow cytometry, metabolic assays, and single-cell RNA sequencing on primary human and mouse macrophages., Results: We identified changes in the vein graft proteome associated with immune responses, lipid metabolism regulated by the PPARs, fatty acid metabolism, matrix remodeling, and hematopoietic cell mobilization. PPARα agonism by pemafibrate retarded the development and inflammation of vein graft lesions in mice, whereas gene silencing worsened plaque formation. Pemafibrate also suppressed arteriovenous fistula lesion development. Metabolomics/lipidomics, functional metabolic assays, and single-cell analysis of cultured human macrophages revealed that PPARα modulates macrophage glycolysis, citrate metabolism, mitochondrial membrane sphingolipid metabolism, and heterogeneity., Conclusions: This study explored potential drivers of vein graft inflammation and identified PPARα as a novel potential pharmacological treatment for this unmet medical need.

In recent years, low-rank matrix recovery problems have attracted much attention in computer vision and machine learning. The corresponding rank minimization problems are both combinational and NP-hard in general, which are mainly solved by both nuclear norm and Schatten-p (0

Recently, there is a rapidly increasing attraction for the efficient recovery of low-rank matrix in computer vision and machine learning. The popular convex solution of rank minimization is nuclear norm-based minimization (NNM), which usually leads to a biased solution since NNM tends to overshrink the rank components and treats each rank component equally. To address this issue, some nonconvex nonsmooth rank (NNR) relaxations have been exploited widely. Different from these convex and nonconvex rank substitutes, this paper first introduces a general and flexible rank relaxation function named weighted NNR relaxation function, which is actually derived from the initial double NNR (DNNR) relaxations, i.e., DNNR relaxation function acts on the nonconvex singular values function (SVF). An iteratively reweighted SVF optimization algorithm with continuation technology through computing the supergradient values to define the weighting vector is devised to solve the DNNR minimization problem, and the closed-form solution of the subproblem can be efficiently obtained by a general proximal operator, in which each element of the desired weighting vector usually satisfies the nondecreasing order. We next prove that the objective function values decrease monotonically, and any limit point of the generated subsequence is a critical point. Combining the Kurdyka-Łojasiewicz property with some milder assumptions, we further give its global convergence guarantee. As an application in the matrix completion problem, experimental results on both synthetic data and real-world data can show that our methods are competitive with several state-of-the-art convex and nonconvex matrix completion methods.

The recent studies have found that the nonconvex relaxation functions usually perform better than the convex counterparts in the l 0 -norm and rank function minimization problems. However, due to the absence of convexity in these nonconvex problems, developing efficient algorithms with convergence guarantee becomes very challenging. Inspired by the basic ideas of both the Jacobian alternating direction method of multipliers (JADMMs) for solving linearly constrained problems with separable objectives and the proximal gradient methods (PGMs) for optimizing the unconstrained problems with one variable, this paper focuses on extending the PGMs to the proximal Jacobian iteration methods (PJIMs) for handling with a family of nonconvex composite optimization problems with two splitting variables. To reduce the total computational complexity by decreasing the number of iterations, we devise the accelerated version of PJIMs through the well-known Nesterov's acceleration strategy and further extend both to solve the multivariable cases. Most importantly, we provide a rigorous convergence analysis, in theory, to show that the generated variable sequence globally converges to a critical point by exploiting the Kurdyka-Łojasiewica (KŁ) property for a broad class of functions. Furthermore, we also establish the linear and sublinear convergence rates of the obtained variable sequence in the objective function. As the specific application to the nonconvex sparse and low-rank recovery problems, several numerical experiments can verify that the newly proposed algorithms not only keep fast convergence speed but also have high precision.

Recently, nuclear norm-based low rank representation (LRR) methods have been popular in several applications, such as subspace segmentation. However, there exist two limitations: one is that nuclear norm as the relaxation of rank function will lead to the suboptimal solution since nuclear norm-based minimization subproblem tends to the over-relaxations of singular value elements and treats each of them equally; the other is that solving LRR problems may cause more time consumption due to involving singular value decomposition of the large scale matrix at each iteration. To overcome both disadvantages, this paper mainly considers two tractable variants of LRR: one is Schatten-p norm minimization-based LRR (i.e., SpNM&#95;LRR) and the other is Schatten-p norm factorization-based LRR (i.e., SpNFLRR) for p=1, 2/3 and 1/2. By introducing two or more auxiliary variables in the constraints, the alternating direction method of multiplier (ADMM) with multiple updating variables can be devised to solve these variants of LRR. Furthermore, both computational complexity and convergence property are given to evaluate nonconvex multiblocks ADMM algorithms. Several experiments finally validate the efficacy and efficiency of our methods on both synthetic data and real world data.

Objective: Despite its large clinical impact, the underlying mechanisms for vein graft failure remain obscure and no effective therapeutic solutions are available. We tested the hypothesis that Notch signaling promotes vein graft disease., Approach and Results: We used 2 biotherapeutics for Delta-like ligand 4 (Dll4), a Notch ligand: (1) blocking antibody and (2) macrophage- or endothelial cell (EC)-targeted small-interfering RNA. Dll4 antibody administration for 28 days inhibited vein graft lesion development in low-density lipoprotein (LDL) receptor-deficient (Ldlr(-/-)) mice, and suppressed macrophage accumulation and macrophage expression of proinflammatory M1 genes. Dll4 antibody treatment for 7 days after grafting also reduced macrophage burden at day 28. Dll4 silencing via macrophage-targeted lipid nanoparticles reduced lesion development and macrophage accumulation, whereas EC-targeted Dll4 small-interfering RNA produced no effects. Gain-of-function and loss-of-function studies suggested in vitro that Dll4 induces proinflammatory molecules in macrophages. Macrophage Dll4 also stimulated smooth muscle cell proliferation and migration and suppressed their differentiation., Conclusions: These results suggest that macrophage Dll4 promotes lesion development in vein grafts via macrophage activation and crosstalk between macrophages and smooth muscle cells, supporting the Dll4-Notch axis as a novel therapeutic target., (© 2015 American Heart Association, Inc.)

Objective: Peroxisome proliferator-activated receptor-γ (PPARγ) has been reported to decrease vascular lesion formation. However, the critical role of vascular smooth muscle cell (VSMC) PPARγ in vascular lesion formation following transplantation is not well understood. In this study, we investigated the role of VSMC PPARγ-mediated signaling in transplantation-associated vascular lesion formation., Methods and Results: Carotid arteries from smooth muscle cell-selective PPARγ knockout (SMPG KO) and wild-type mice were transplanted to CBA/CaJ recipient mice. The recipient mice received a control diet or pioglitazone-containing diet. Pioglitazone reduced vascular lesion formation in transplanted wild-type, but not in SMPG KO carotid arteries. Histological analysis suggested that PPARγ attenuates vascular lesion formation through antiinflammatory signaling, as evidenced by the increase of intimal inflammatory cells and tumor necrosis factor-α expression in SMPG KO allografts. Intravital microscopy revealed increased inflammatory cell rolling and attachment to endothelial cells in small blood vessels of SMPG KO mice following cytokine stimulation. SMPG KO mice, as shown by Western blotting, have elevated vascular cell adhesion molecule-1 (VCAM-1) expression. Furthermore, immunohistochemistry demonstrated SMPG KO allografts have increased VCAM-1., Conclusions: Loss of PPARγ in VSMC promotes transplantation-associated vascular lesion formation through increased VCAM-1 expression. VSMC PPARγ also mediates pioglitazone-reduced vascular lesion formation.

Tissue injury in mammals triggers both inflammatory and repair responses that, in some contexts, results in fibrosis. Fibrosis is characterized by the persistence of activated myofibroblasts, ineffective re-epithelialization, and variable degrees of inflammation within injured tissues. The protein kinase inhibitor (PKI), imatinib mesylate, has been proposed as a potential antifibrotic therapeutic agent. In this study, the efficacy of imatinib mesylate to modulate fibrogenic responses, both in vitro and in vivo, was examined. In an in vitro fibroblast culture model, imatinib inhibits platelet-derived growth factor receptor activation and fibroblast proliferation but not the stably differentiated myofibroblast phenotype. Furthermore, imatinib inhibits lung epithelial cell proliferation and survival but not the induction of epithelial-mesenchymal transition. Imatinib does not alter transforming growth factor-beta/SMAD3 signaling in either cell type. In a murine model of lung fibrosis, bleomycin-induced injury to the pulmonary epithelium provokes an early inflammatory response with more delayed fibrosis during the late reparative phase of lung injury. Imatinib mesylate (10 mg/kg/day by i.p. injection or oral gavage), administered during the postinjury repair phase, failed to significantly alter fibrogenic responses assessed by histopathology, collagen content, and the accumulation of myofibroblasts within the injured lung. These studies indicate that the capacity of a PKI to inhibit fibroblast proliferation may be insufficient to mediate significant antifibrotic effects in late stages of tissue injury repair. Pharmacologic agents that modulate the activities and fate of differentiated (myo)fibroblasts, without interfering with the regenerative capacity of epithelial cells, are likely to be more effective for treatment of nonresolving, progressive fibrotic disorders.

Dentin sialophosphoprotein (DSPP) is a major secretory product of odontoblasts and is critical for proper dentin formation. DSPP is believed to be processed into only two structural/functional domains: dentin sialoprotein (DSP) and dentin phosphoprotein (DPP). Here we report the isolation and characterization of a third domain of DSPP, designated dentin glycoprotein (DGP). DGP was isolated from a guanidine/EDTA extract of porcine tooth dentin by ion exchange, hydroxyapatite affinity, size exclusion, and RP-HPL chromatography. Endoproteinase lysine C digestion products of DGP were characterized by Edman sequencing and mass spectrometry. The porcine DGP backbone is the 81-amino acid segment of DSPP (Ser392 to Gly472) between the DSP and DPP domains. DGP has four phosphorylated serine residues (Ser453, Ser455, Ser457, and Ser462) and one glycosylated asparagine (Asn397). There are no other post-translational modifications. DGP is a stains-all positive protein with an apparent molecular mass on SDS-PAGE of 19 kDa, which is reduced by glycopeptidase A digestion to 16 kDa. A variety of glycans can be linked to Asn397. All are complex biantennary structures with a common N-linked pentasaccharide core (mannose3-N-acetylglucosamine2), most with a fucosyl residue on the innermost N-acetylglucosamine. The alpha1-3 and alpha1-6 arms are always galactose beta1-4 N-acetylglucosamine beta1-2 mannose, and either or both arms can be unsialidated or monosialidated. The calculated monoisotopic molecular masses of the different glycosylated forms of the DGP phosphoprotein are: unsialidated 10,523 and 10,670, monosialidated 10,815 and 10,961, and disialidated 11,106, and 11,252 Da, with the disialidated forms being the most abundant.

Progressive fibrotic diseases involving diverse organ systems are associated with the persistence of fibroblasts/myofibroblasts in injured tissues. Activation of focal adhesion kinase (FAK) and protein kinase B (PKB/Akt) by transforming growth factor-beta1 mediate stable induction of myofibroblast differentiation and survival. In this report, we demonstrate that transforming growth factor-beta1-induced activation of both PKB/Akt and FAK are dose dependently inhibited by the protein kinase inhibitor, AG1879, in cultured human lung fibroblasts. In a murine model of intratracheal bleomycin-induced lung fibrosis, regions of active fibrogenesis demonstrate elevated expression of PKB/Akt and FAK phosphorylation in vivo, effects that are attenuated in mice receiving daily intraperitoneal injections of AG1879 (bleomycin-AG1879) versus a chemically inactive analog (bleomycin-control). PKB/Akt and FAK phosphorylation are elevated in fibroblasts isolated from lungs of bleomycin-injured mice, effects that are inhibited in bleomycin-AG1879 mice. Accumulation of alpha-smooth muscle actin-expressing myofibroblasts is markedly reduced in lungs of bleomycin-AG1879 mice. The numbers of recruited inflammatory cells were not significantly different between these groups. Bleomycin-AG1879 mice are protected from lung fibrosis as evidenced by histopathology, trichrome staining, and biochemical analysis for collagen. Thus, targeting of prosurvival signaling pathways in fibroblasts/myofibroblasts may provide a novel and effective strategy for anti-fibrotic therapy of treatment-unresponsive fibrotic disorders.

Dentin sialoprotein (DSP) is a glycoprotein that is critical for proper tooth dentin formation, but little is known about the nature of its carbohydrate attachments and other post-translational modifications. We have isolated DSP from pig dentin and demonstrate that it is a proteoglycan. Polyclonal antibodies were raised in chicken against recombinant pig DSP, and used to identify native DSP in fractions of tooth dentin proteins extracted from developing pig molars. Amino acid analyses and characterization of lysylendopeptidase cleavage products confirmed that the purified protein was DSP, and that Arg391 is at the DSP C terminus. On SDS-PAGE and on urea gels, DSP appeared as a smear extending from 280 to 100 kDa, but in the presence of beta-mercaptoethanol the top of the DSP smear disappeared. The high molecular weight material was likely comprised of covalent DSP dimers connected by a disulfide bridge at Cys205. Oligosaccharides were released from DSP following N- and O-linked glycosidase digestions, but these digestions had little effect on the apparent molecular weight of DSP on SDS-PAGE, when compared with the significant reduction following chondroitinase ABC digestion. Glycosaminoglycanases with assorted glycosaminoglycan (GAG) cleavage specificities coupled with Western analyses of the cleaved GAG "stubs" demonstrated that the DSP GAG attachments contain chondroitin 6-sulfate, but not keratan sulfate, heparan sulfate, chondroitin, or chondroitin 4-sulfate. DSP binds biotin-labeled hyaluronic acid, and such binding is inhibited by the addition of unlabeled hyaluronic acid. We conclude that DSP is a proteoglycan and that GAG attachments are the predominant structural feature of porcine DSP.

Transforming growth factor-beta1 (TGF-beta1) is a multifunctional cytokine involved in differentiation, growth, and survival of mesenchymal cells while inhibiting growth/survival of most other cell types. The mechanism(s) of pro-survival signaling by TGF-beta1 in mesenchymal cells is unclear. In this report, we demonstrate that TGF-beta1 protects against serum deprivation-induced apoptosis of mesenchymal cells isolated from patients with acute lung injury and of normal human fetal lung fibroblasts (IMR-90). TGF-beta receptor(s)-activated signaling in these cells involves rapid activation of the Smad and p38 MAPK pathways within minutes of TGF-beta1 treatment followed by a more delayed activation of the pro-survival phosphatidylinositol 3-kinase-protein kinase B (PKB)/Akt pathway. Pharmacological inhibition of p38 MAPK with SB203580 or expression of a p38 kinase-deficient mutant protein inhibits TGF-beta1-induced PKB/Akt phosphorylation. Conditioned medium from TGF-beta1-treated cells rapidly induces PKB/Akt activation in an SB203580- and suramin-sensitive manner, suggesting p38 MAPK-dependent production of a secreted growth factor that activates this pro-survival pathway by an autocrine/paracrine mechanism. Inhibition of the phosphatidylinositol 3-kinase-PKB/Akt pathway blocks TGF-beta1-induced resistance to apoptosis. These results demonstrate the activation of a novel TGF-beta1-activated pro-survival/anti-apoptotic signaling pathway in mesenchymal cells/fibroblasts that may explain cell-specific actions of TGF-beta1 and provide mechanistic insights into its pro-fibrotic and tumor-promoting effects.