In Vivo Mechanical Loading Modulates Insulin-Like Growth Factor Binding Protein-2 Gene Expression in Rat Osteocytes

Mechanical stimulation is essential for maintaining skeletal integrity. Mechanosensitive osteocytes are important during the osteogenic response. The growth hormone-insulin-like growth factor (GH-IGF) axis plays a key role during regulation of bone formation and remodeling. Insulin-like growth factor binding proteins (IGFBPs) are able to modulate IGF activity. The aim of this study was to characterize the role of IGFBP-2 in the translation of mechanical stimuli into bone formation locally in rat tibiae. Female Wistar rats were assigned to three groups (n = 5): load, sham, and control. The four-point bending model was used to induce a single period of mechanical loading on the tibial shaft. The effect on IGFBP-2 mRNA expression 6 hours after stimulation was determined with nonradioactive in situ hybridization on decalcified tibial sections. Endogenous IGFBP-2 mRNA was expressed in trabecular and cortical osteoblasts, some trabecular and subendocortical osteocytes, intracortical endothelial cells of blood vessels, and periosteum. Megakaryocytes, macrophages, and myeloid cells also expressed IGFBP-2 mRNA. Loading and sham loading did not affect IGFBP-2 mRNA expression in osteoblasts, bone marrow cells, and chondrocytes. An increase of IGFBP-2 mRNA-positive osteocytes was shown in loaded (1.68-fold) and sham-loaded (1.35-fold) endocortical tibial shaft. In conclusion, 6 hours after a single loading session, the number of IGFBP-2 mRNA-expressing osteocytes at the endosteal side of the shaft and inner lamellae was increased in squeezed and bended tibiae. Mechanical stimulation modulates IGFBP-2 mRNA expression in endocortical osteocytes. We suggest that IGFBP-2 plays a role in the lamellar bone formation process

TOPS++FATCAT: Fast flexible structural alignment using constraints derived from TOPS+ Strings Model

<p>Abstract</p> <p>Background</p> <p>Protein structure analysis and comparison are major challenges in structural bioinformatics. Despite the existence of many tools and algorithms, very few of them have managed to capture the intuitive understanding of protein structures developed in structural biology, especially in the context of rapid database searches. Such intuitions could help speed up similarity searches and make it easier to understand the results of such analyses.</p> <p>Results</p> <p>We developed a TOPS++FATCAT algorithm that uses an intuitive description of the proteins' structures as captured in the popular TOPS diagrams to limit the search space of the aligned fragment pairs (AFPs) in the flexible alignment of protein structures performed by the FATCAT algorithm. The TOPS++FATCAT algorithm is faster than FATCAT by more than an order of magnitude with a minimal cost in classification and alignment accuracy. For beta-rich proteins its accuracy is better than FATCAT, because the TOPS+ strings models contains important information of the parallel and anti-parallel hydrogen-bond patterns between the beta-strand SSEs (Secondary Structural Elements). We show that the TOPS++FATCAT errors, rare as they are, can be clearly linked to oversimplifications of the TOPS diagrams and can be corrected by the development of more precise secondary structure element definitions.</p> <p>Software Availability</p> <p>The benchmark analysis results and the compressed archive of the TOPS++FATCAT program for Linux platform can be downloaded from the following web site: <url>http://fatcat.burnham.org/TOPS/</url></p> <p>Conclusion</p> <p>TOPS++FATCAT provides FATCAT accuracy and insights into protein structural changes at a speed comparable to sequence alignments, opening up a possibility of interactive protein structure similarity searches.</p

Inhibition of TGF-β Signaling and Decreased Apoptosis in IUGR-Associated Lung Disease in Rats

Intrauterine growth restriction is associated with impaired lung function in adulthood. It is unknown whether such impairment of lung function is linked to the transforming growth factor (TGF)-β system in the lung. Therefore, we investigated the effects of IUGR on lung function, expression of extracellular matrix (ECM) components and TGF-β signaling in rats. IUGR was induced in rats by isocaloric protein restriction during gestation. Lung function was assessed with direct plethysmography at postnatal day (P) 70. Pulmonary activity of the TGF-β system was determined at P1 and P70. TGF-β signaling was blocked in vitro using adenovirus-delivered Smad7. At P70, respiratory airway compliance was significantly impaired after IUGR. These changes were accompanied by decreased expression of TGF-β1 at P1 and P70 and a consistently dampened phosphorylation of Smad2 and Smad3. Furthermore, the mRNA expression levels of inhibitors of TGF-β signaling (Smad7 and Smurf2) were reduced, and the expression of TGF-β-regulated ECM components (e.g. collagen I) was decreased in the lungs of IUGR animals at P1; whereas elastin and tenascin N expression was significantly upregulated. In vitro inhibition of TGF-β signaling in NIH/3T3, MLE 12 and endothelial cells by adenovirus-delivered Smad7 demonstrated a direct effect on the expression of ECM components. Taken together, these data demonstrate a significant impact of IUGR on lung development and function and suggest that attenuated TGF-β signaling may contribute to the pathological processes of IUGR-associated lung disease

Using structural motif descriptors for sequence-based binding site prediction

All authors are with the Biotechnological Center, TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany and -- Wan Kyu Kim is with the Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USABackground: Many protein sequences are still poorly annotated. Functional characterization of a protein is often improved by the identification of its interaction partners. Here, we aim to predict protein-protein interactions (PPI) and protein-ligand interactions (PLI) on sequence level using 3D information. To this end, we use machine learning to compile sequential segments that constitute structural features of an interaction site into one profile Hidden Markov Model descriptor. The resulting collection of descriptors can be used to screen sequence databases in order to predict functional sites. -- Results: We generate descriptors for 740 classified types of protein-protein binding sites and for more than 3,000 protein-ligand binding sites. Cross validation reveals that two thirds of the PPI descriptors are sufficiently conserved and significant enough to be used for binding site recognition. We further validate 230 PPIs that were extracted from the literature, where we additionally identify the interface residues. Finally we test ligand-binding descriptors for the case of ATP. From sequences with Swiss-Prot annotation "ATP-binding", we achieve a recall of 25% with a precision of 89%, whereas Prosite's P-loop motif recognizes an equal amount of hits at the expense of a much higher number of false positives (precision: 57%). Our method yields 771 hits with a precision of 96% that were not previously picked up by any Prosite-pattern. -- Conclusion: The automatically generated descriptors are a useful complement to known Prosite/InterPro motifs. They serve to predict protein-protein as well as protein-ligand interactions along with their binding site residues for proteins where merely sequence information is available.Institute for Cellular and Molecular [email protected]

Appropriateness of acute admissions and last in-patient day for patients with long term neurological conditions

<p>Abstract</p> <p>Background</p> <p>To examine the appropriateness of admissions and in-patient stay for patients with long term neurological conditions (LTNCs). To identify variables predictive of appropriateness and explore management alternatives.</p> <p>Methods</p> <p>Adults admitted as acute patients to Derby Hospitals NHS Foundation Trust (England). Data were collected prospectively and examined by a multi-disciplinary expert panel to determine the appropriateness of admission and length of stay (LoS). Management alternatives were discussed.</p> <p>Results</p> <p>A total of 119 participants were recruited. 32 admissions were inappropriate and 83 were for an inappropriate duration. Whether a participant lived in their own home was predictive of an inappropriate admission. The number of LTNCs, number of presenting complaints and whether the participant lived alone in their own home were predictive of an inappropriate LoS. For admissions judged to be inappropriate, the panel suggested management alternatives.</p> <p>Conclusion</p> <p>Patients with LTNCs are being admitted to hospital when other services, e.g. ambulatory care, are available which could meet their needs. Inefficiencies in hospital procedures, such as discharge planning and patient transfers, continue to exist. Recognition of the need to plan for discharge at admission and to ensure in-patient services are provided in a timely manner may contribute towards improved efficiency.</p

Is EC class predictable from reaction mechanism?

We thank the Scottish Universities Life Sciences Alliance (SULSA) and the Scottish Overseas Research Student Awards Scheme of the Scottish Funding Council (SFC) for financial support.Background: We investigate the relationships between the EC (Enzyme Commission) class, the associated chemical reaction, and the reaction mechanism by building predictive models using Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbours (kNN). We consider two ways of encoding the reaction mechanism in descriptors, and also three approaches that encode only the overall chemical reaction. Both cross-validation and also an external test set are used. Results: The three descriptor sets encoding overall chemical transformation perform better than the two descriptions of mechanism. SVM and RF models perform comparably well; kNN is less successful. Oxidoreductases and hydrolases are relatively well predicted by all types of descriptor; isomerases are well predicted by overall reaction descriptors but not by mechanistic ones. Conclusions: Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. Oxidoreductases, hydrolases, and to some extent isomerases and ligases, have clear chemical signatures, making them easier to predict than transferases and lyases. We find evidence that isomerases as a class are notably mechanistically diverse and that their one shared property, of substrate and product being isomers, can arise in various unrelated ways. The performance of the different machine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roughly equally effective. kNN is less successful, given the role that non-local information plays in successful classification. We note also that, despite a lack of clarity in the literature, EC number prediction is not a single problem; the challenge of predicting protein function from available sequence data is quite different from assigning an EC classification from a cheminformatics representation of a reaction.Publisher PDFPeer reviewe

Structural motifs recurring in different folds recognize the same ligand fragments

<p>Abstract</p> <p>Background</p> <p>The structural analysis of protein ligand binding sites can provide information relevant for assigning functions to unknown proteins, to guide the drug discovery process and to infer relations among distant protein folds. Previous approaches to the comparative analysis of binding pockets have usually been focused either on the ligand or the protein component. Even though several useful observations have been made with these approaches they both have limitations. In the former case the analysis is restricted to binding pockets interacting with similar ligands, while in the latter it is difficult to systematically check whether the observed structural similarities have a functional significance.</p> <p>Results</p> <p>Here we propose a novel methodology that takes into account the structure of both the binding pocket and the ligand. We first look for local similarities in a set of binding pockets and then check whether the bound ligands, even if completely different, share a common fragment that can account for the presence of the structural motif. Thanks to this method we can identify structural motifs whose functional significance is explained by the presence of shared features in the interacting ligands.</p> <p>Conclusion</p> <p>The application of this method to a large dataset of binding pockets allows the identification of recurring protein motifs that bind specific ligand fragments, even in the context of molecules with a different overall structure. In addition some of these motifs are present in a high number of evolutionarily unrelated proteins.</p

Tableau-based protein substructure search using quadratic programming

<p>Abstract</p> <p>Background</p> <p>Searching for proteins that contain similar substructures is an important task in structural biology. The exact solution of most formulations of this problem, including a recently published method based on tableaux, is too slow for practical use in scanning a large database.</p> <p>Results</p> <p>We developed an improved method for detecting substructural similarities in proteins using tableaux. Tableaux are compared efficiently by solving the quadratic program (QP) corresponding to the quadratic integer program (QIP) formulation of the extraction of maximally-similar tableaux. We compare the accuracy of the method in classifying protein folds with some existing techniques.</p> <p>Conclusion</p> <p>We find that including constraints based on the separation of secondary structure elements increases the accuracy of protein structure search using maximally-similar subtableau extraction, to a level where it has comparable or superior accuracy to existing techniques. We demonstrate that our implementation is able to search a structural database in a matter of hours on a standard PC.</p

Sex differences in the relative contribution of social and clinical factors to the Health Utilities Index Mark 2 measure of health-related quality of life in older home care clients

Abstract Background The heterogeneity evident among home care clients highlights the need for greater understanding of the clinical and social determinants of multi-dimensional health-related quality of life (HRQL) indices and of potential sex-differences in these determinants. We examined the relative contribution of social and clinical factors to HRQL among older home care clients and explored whether any of the observed associations varied by sex. Methods The Canadian-US sample included 514 clients. Self-reported HRQL was measured during in-home interviews (2002-04) using the Health Utilities Index Mark 2 (HUI2). Data on clients' sociodemographic, health and clinical characteristics were obtained with the Minimum Data Set for Home Care. The relative associations between clients' characteristics and HUI2 scores were examined using multivariable linear regression models. Results Women had a significantly lower mean HUI2 score than men (0.48, 95%CI 0.46-0.50 vs. 0.52, 0.49-0.55). Clients with distressed caregivers and poor self-rated health exhibited significantly lower HRQL scores after adjustment for a comprehensive list of clinical conditions. Several other factors remained statistically significant (arthritis, psychiatric illness, bladder incontinence, urinary tract infection) or clinically important (reported loneliness, congestive heart failure, pressure ulcers) correlates of lower HUI2 scores in adjusted analyses. These associations generally did not vary significantly by sex. Conclusion For females and males, HRQL scores were negatively associated with conditions predictive or indicative of disability and with markers of psychosocial stress. Despite sex differences in the prevalence of social and clinical factors likely to affect HRQL, few varied significantly by sex in their relative impact on HUI2 scores. Further exploration of differences in the relative importance of clinical and psychosocial well-being (e.g., loneliness) to HRQL among female and male clients may help guide the development of sex-specific strategies for risk screening and care management

Disrupting the rhythm of depression using Mobile Cognitive Therapy for recurrent depression: randomized controlled trial design and protocol

Background: Major depressive disorder (MDD) is projected to rank second on a list of 15 major diseases in terms of burden in 2030. The major contribution of MDD to disability and health care costs is largely due to its highly recurrent nature. Accordingly, efforts to reduce the disabling effects of this chronic condition should shift to preventing recurrence, especially in patients at high risk of recurrence. Given its high prevalence and the fact that interventions are necessary during the remitted phase, new approaches are needed to prevent relapse in depression. Methods/design: The best established effective and available psychological intervention is cognitive therapy. However, it is costly and not available for most patients. Therefore, we will compare the effectiveness and cost-effectiveness of self-management supported by online CT accompanied by SMS based tele-monitoring of depressive symptomatology, i.e. Mobile Cognitive Therapy (M-CT) versus treatment as us usual (TAU). Remitted patients (n = 268) with at least two previous depressive episodes will be recruited and randomized over (1) M-CT in addition to TAU versus (2) TAU alone, with follow-ups at 3, 12, and 24 months. Randomization will be stratified for number of previous episodes and type of treatment as usual. Primary outcome is time until relapse/recurrence over 24 months using DSM-IV-TR criteria as assessed by the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). For the economic evaluation the balance between costs and health outcomes will be compared across strategies using a societal perspective. Discussion: Internet-based interventions might be helpful in empowering patients to become their own disease managers in this lifelong recurrent disorder. This is, as far as we are aware of, the first study that examines the (cost) effectiveness of an E-mental health program using SMS monitoring of symptoms with therapist support to prevent relapse in remitted recurrently depressed patient