Putative functions of extracellular matrix glycoproteins in secondary palate morphogenesis

Cleft palate is a common birth defect in humans. Elevation and fusion of paired palatal shelves are coordinated by growth and transcription factors, and mutations in these can cause malformations. Among the effector genes for growth factor signaling are extracellular matrix (ECM) glycoproteins. These provide substrates for cell adhesion (e.g., fibronectin, tenascins), but also regulate growth factor availability (e.g., fibrillins). Cleft palate in Bmp7 null mouse embryos is caused by a delay in palatal shelf elevation. In contrast, palatal shelves of Tgf-β3 knockout mice elevate normally, but a cleft develops due to their failure to fuse. However, nothing is known about a possible functional interaction between specific ECM proteins and Tgf-β/Bmp family members in palatogenesis. To start addressing this question, we studied the mRNA and protein distribution of relevant ECM components during secondary palate development, and compared it to growth factor expression in wildtypewild type and mutant mice. We found that fibrillin-2 (but not fibrillin-1) mRNA appeared in the mesenchyme of elevated palatal shelves adjacent to the midline epithelial cells, which were positive for Tgf-β3 mRNA. Moreover, midline epithelial cells started expressing fibronectin upon contact of the two palatal shelves. These findings support the hypothesis that fibrillin-2 and fibronectin are involved in regulating the activity of Tgf-β3 at the fusing midline. In addition, we observed that tenascin-W (but not tenascin-C) was misexpressed in palatal shelves of Bmp7-deficient mouse embryos. In contrast to tenascin-C, tenascin-W secretion was strongly induced by Bmp7 in embryonic cranial fibroblasts in vitro. These results are consistent with a putative function for tenascin-W as a target of Bmp7 signaling during palate elevation. Our results indicate that distinct ECM proteins are important for morphogenesis of the secondary palate, both as downstream effectors and as regulators of Tgf-β/Bmp activity.

Angiopoietins assemble distinct Tie2 signalling complexes in endothelial cell-cell and cell-matrix contacts

The receptor tyrosine kinase Tie2, and its activating ligand Angiopoietin-1 (Ang1), are required for vascular remodelling and vessel integrity, whereas Ang2 may counteract these functions. However, it is not known how Tie2 transduces these different signals. Here, we show that Ang1 induces unique Tie2 complexes in mobile and confluent endothelial cells. Matrix-bound Ang1 induced cell adhesion, motility and Tie2 activation in cell-matrix contacts that became translocated to the trailing edge in migrating endothelial cells. In contrast, in contacting cells Ang1 induced Tie2 translocation to cell-cell contacts and the formation of homotypic Tie2-Tie2 trans-associated complexes that included the vascular endothelial phosphotyrosine phosphatase, leading to inhibition of paracellular permeability. Distinct signalling proteins were preferentially activated by Tie2 in the cell-matrix and cell-cell contacts, where Ang2 inhibited Ang1-induced Tie2 activation. This novel type of cellular microenvironment-dependent receptor tyrosine kinase activation may explain some of the effects of angiopoietins in angiogenesis and vessel stabilization.

Variation of cell and matrix morphologies in articular cartilage among locations in the adult human knee

OBJECTIVE: Understanding of articular cartilage physiology, remodelling mechanisms, and evaluation of tissue engineering repair methods requires reference information regarding normal structural organization. Our goals were to examine the variation of cartilage cell and matrix morphology in different topographical areas of the adult human knee joint. METHODS: Osteochondral explants were acquired from seven distinct anatomical locations of the knee joints of deceased persons aged 20-40 years and prepared for analysis of cell, matrix and tissue morphology using confocal microscopy and unbiased stereological methods. Differences between locations were identified by statistical analysis. RESULTS: Medial femoral condyle cartilage had relatively high cell surface area per unit tissue volume in the superficial zone. In the transitional zone, meniscus-covered lateral tibia cartilage showed elevated chondrocyte densities compared to the rest of the knee while lateral femoral condyle cartilage exhibited particularly large chondrocytes. Statistical analyses indicated highly uniform morphology throughout the radial zone (lower 80% of cartilage thickness) in the knee, and strong similarities in cell and matrix morphologies among cartilage from the femoral condyles and also in the mediocentral tibial plateau. Throughout the adult human knee, the mean matrix volume per chondron was remarkably constant at approximately 224,000 microm(3), corresponding to approximately 4.6 x 10(6) chondrons per cm(3). CONCLUSIONS: The uniformity of matrix volume per chondron throughout the adult human knee suggests that cell-scale biophysical and metabolic constraints may place limitations on cartilage thickness, mechanical properties, and remodelling mechanisms. Data may also aid the evaluation of cartilage tissue engineering treatments in a site-specific manner. Results indicate that joint locations which perform similar biomechanical functions have similar cell and matrix morphologies; findings may therefore also provide clues to understanding conditions under which focal lesions leading to osteoarthritis may occur.

Matrix metalloproteinases: multifunctional effectors of inflammation in multiple sclerosis and bacterial meningitis

Matrix metalloproteinases (MMPs) are a family of Zn2+-dependent endopeptidases targeting extracellular matrix (ECM) compounds as well as a number of other proteins. Their proteolytic activity acts as an effector mechanism of tissue remodeling in physiologic and pathologic conditions, and as modulator of inflammation. In the context of neuro-inflammatory diseases, MMPs have been implicated in processes such as (a) blood-brain barrier (BBB) and blood-nerve barrier opening, (b) invasion of neural tissue by blood-derived immune cells, (c) shedding of cytokines and cytokine receptors, and (d) direct cellular damage in diseases of the peripheral and central nervous system. This review focuses on the role of MMPs in multiple sclerosis (MS) and bacterial meningitis (BM), two neuro-inflammatory diseases where current therapeutic approaches are insufficient to prevent severe disability in the majority of patients. Inhibition of enzymatic activity may prevent MMP-mediated neuronal damage due to an overactive or deviated immune response in both diseases. Downregulation of MMP release may be the molecular basis for the beneficial effect of IFN-beta and steroids in MS. Instead, synthetic MMP inhibitors offer the possibility to shut off enzymatic activity of already activated MMPs. In animal models of MS and BM, they efficiently attenuated clinical disease symptoms and prevented brain damage due to excessive metalloproteinase activity. However, the required target profile for the therapeutic use of this novel group of compounds in human disease is not yet sufficiently defined and may be different depending on the type and stage of disease. Currently available MMP inhibitors show little target-specificity within the MMP family and may lead to side-effects due to interference with physiological functions of MMPs. Results from human MS and BM indicate that only a restricted number of MMPs specific for each disease is up-regulated. MMP inhibitors with selective target profiles offer the possibility of a more efficient therapy of MS and BM and may enter clinical trials in the near future.

Dominance of chemokine ligand 2 and matrix metalloproteinase-2 and -9 and suppression of pro-inflammatory cytokines in the epidural compartment after intervertebral disc extrusion in a canine model

BACKGROUND CONTEXT In canine intervertebral disc (IVD) disease, a useful animal model, only little is known about the inflammatory response in the epidural space. PURPOSE To determine messenger RNA (mRNA) expressions of selected cytokines, chemokines, and matrix metalloproteinases (MMPs) qualitatively and semiquantitatively over the course of the disease and to correlate results to neurologic status and outcome. STUDY DESIGN/SETTING Prospective study using extruded IVD material of dogs with thoracolumbar IVD extrusion. PATIENT SAMPLE Seventy affected and 13 control (24 samples) dogs. OUTCOME MEASURES Duration of neurologic signs, pretreatment, neurologic grade, severity of pain, and outcome were recorded. After diagnostic imaging, decompressive surgery was performed. METHODS Messenger RNA expressions of interleukin (IL)-1β, IL-2, IL-4, IL-6, IL-8, IL-10, tumor necrosis factor (TNF), interferon (IFN)γ, MMP-2, MMP-9, chemokine ligand (CCL)2, CCL3, and three housekeeping genes was determined in the collected epidural material by Panomics 2.0 QuantiGene Plex technology. Relative mRNA expression and fold changes were calculated. Relative mRNA expression was correlated statistically to clinical parameters. RESULTS Fold changes of TNF, IL-1β, IL-2, IL-4, IL-6, IL-10, IFNγ, and CCL3 were clearly downregulated in all stages of the disease. MMP-9 was downregulated in the acute stage and upregulated in the subacute and chronic phase. Interleukin-8 was upregulated in acute cases. MMP-2 showed mild and CCL2 strong upregulation over the whole course of the disease. In dogs with severe pain, CCL3 and IFNγ were significantly higher compared with dogs without pain (p=.017/.020). Dogs pretreated with nonsteroidal anti-inflammatory drugs revealed significantly lower mRNA expression of IL-8 (p=.017). CONCLUSIONS The high CCL2 levels and upregulated MMPs combined with downregulated T-cell cytokines and suppressed pro-inflammatory genes in extruded canine disc material indicate that the epidural reaction is dominated by infiltrating monocytes differentiating into macrophages with tissue remodeling functions. These results will help to understand the pathogenic processes representing the basis for novel therapeutic approaches. The canine IVD disease model will be rewarding in this process.

The block numerical range of analytic operator functions

We introduce the block numerical range Wn(L) of an operator function L with respect to a decomposition H = H1⊕. . .⊕Hn of the underlying Hilbert space. Our main results include the spectral inclusion property and estimates of the norm of the resolvent for analytic L . They generalise, and improve, the corresponding results for the numerical range (which is the case n = 1) since the block numerical range is contained in, and may be much smaller than, the usual numerical range. We show that refinements of the decomposition entail inclusions between the corresponding block numerical ranges and that the block numerical range of the operator matrix function L contains those of its principal subminors. For the special case of operator polynomials, we investigate the boundedness of Wn(L) and we prove a Perron-Frobenius type result for the block numerical radius of monic operator polynomials with coefficients that are positive in Hilbert lattice sense.

Multiple functions of gingival and mucoperiosteal fibroblasts in oral wound healing and repair

Fibroblasts are cells of mesenchymal origin. They are responsible for the production of most extracellular matrix in connective tissues and are essential for wound healing and repair. In recent years, it has become clear that fibroblasts from different tissues have various distinct traits. Moreover, wounds in the oral cavity heal under very special environmental conditions compared with skin wounds. Here, we reviewed the current literature on the various interconnected functions of gingival and mucoperiosteal fibroblasts during the repair of oral wounds. The MEDLINE database was searched with the following terms: (gingival OR mucoperiosteal) AND fibroblast AND (wound healing OR repair). The data gathered were used to compare oral fibroblasts with fibroblasts from other tissues in terms of their regulation and function during wound healing. Specifically, we sought answers to the following questions: (i) what is the role of oral fibroblasts in the inflammatory response in acute wounds; (ii) how do growth factors control the function of oral fibroblasts during wound healing; (iii) how do oral fibroblasts produce, remodel and interact with extracellular matrix in healing wounds; (iv) how do oral fibroblasts respond to mechanical stress; and (v) how does aging affect the fetal-like responses and functions of oral fibroblasts? The current state of research indicates that oral fibroblasts possess unique characteristics and tightly controlled specific functions in wound healing and repair. This information is essential for developing new strategies to control the intraoral wound-healing processes of the individual patient.

MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().