Platelet-Rich Plasma combined with a sterile 3D polylactic acid scaffold for postoperative management of complete hoof wall resection for keratoma in four horses.

Keratoma is a non-malignant horse tumour that grows in the space between the horn of the hoof and the distal phalanx. Keratoma causes lameness in the horse, and surgical excision is the treatment of choice. Four horses underwent removal of a keratoma by complete hoof wall resection. The remaining wound was treated with Platelet-Rich Plasma (PRP) combined with a sterile 3D polylactic acid scaffold. The PRP was applied at 3, 6, 9, 12, 15 and 18 days postoperatively. The surgical site was cleaned with gauzes and swabs soaked in Ringer’s lactate solution before applying PRP and the foot bandage. Healthy granulation tissue developed at 6-21 days postoperatively. The hoof wall defect was completely filled with new hoof wall within 6-8 months after surgery. All horses returned to their previous exercise level and no recurrence of lameness was reported by the owner

Crystal structure of SEL1L: Insight into the roles of SLR motifs in ERAD pathway

Terminally misfolded proteins are selectively recognized and cleared by the endoplasmic reticulum-associated degradation (ERAD) pathway. SEL1L, a component of the ERAD machinery, plays an important role in selecting and transporting ERAD substrates for degradation. We have determined the crystal structure of the mouse SEL1L central domain comprising five Sel1-Like Repeats (SLR motifs 5 to 9; hereafter called SEL1Lcent). Strikingly, SEL1Lcent forms a homodimer with two-fold symmetry in a head-to-tail manner. Particularly, the SLR motif 9 plays an important role in dimer formation by adopting a domain-swapped structure and providing an extensive dimeric interface. We identified that the full-length SEL1L forms a self-oligomer through the SEL1Lcent domain in mammalian cells. Furthermore, we discovered that the SLR-C, comprising SLR motifs 10 and 11, of SEL1L directly interacts with the N-terminus luminal loops of HRD1. Therefore, we propose that certain SLR motifs of SEL1L play a unique role in membrane bound ERAD machinery.ope

Clustering of gene expression data: performance and similarity analysis

BACKGROUND: DNA Microarray technology is an innovative methodology in experimental molecular biology, which has produced huge amounts of valuable data in the profile of gene expression. Many clustering algorithms have been proposed to analyze gene expression data, but little guidance is available to help choose among them. The evaluation of feasible and applicable clustering algorithms is becoming an important issue in today's bioinformatics research. RESULTS: In this paper we first experimentally study three major clustering algorithms: Hierarchical Clustering (HC), Self-Organizing Map (SOM), and Self Organizing Tree Algorithm (SOTA) using Yeast Saccharomyces cerevisiae gene expression data, and compare their performance. We then introduce Cluster Diff, a new data mining tool, to conduct the similarity analysis of clusters generated by different algorithms. The performance study shows that SOTA is more efficient than SOM while HC is the least efficient. The results of similarity analysis show that when given a target cluster, the Cluster Diff can efficiently determine the closest match from a set of clusters. Therefore, it is an effective approach for evaluating different clustering algorithms. CONCLUSION: HC methods allow a visual, convenient representation of genes. However, they are neither robust nor efficient. The SOM is more robust against noise. A disadvantage of SOM is that the number of clusters has to be fixed beforehand. The SOTA combines the advantages of both hierarchical and SOM clustering. It allows a visual representation of the clusters and their structure and is not sensitive to noises. The SOTA is also more flexible than the other two clustering methods. By using our data mining tool, Cluster Diff, it is possible to analyze the similarity of clusters generated by different algorithms and thereby enable comparisons of different clustering methods

Odorant binding proteins : a biotechnological tool for odour control

The application of an odorant binding protein for odour control and fragrance delayed release from a textile surface was first explored in this work. Pig OBP-1 gene was cloned and expressed in Escherichia coli , and the purified protein was biochemically characterized. The IC50 values(concentrations of competitor that caused a decay of fluorescence to half-maximal intensity) were determined for four distinct fragrances, namely, citronellol, benzyl benzoate,citronellyl valerate and ethyl valerate. The results showed a strong binding of citronellyl valerate,citronellol and benzyl benzoate to the recombinant protein, while ethyl valerate displayed weaker binding. Cationized cotton substrates were coated with porcine odorant binding protein and tested for their capacity to retain citronellol and to mask the smell of cigarette smoke. The immobilized protein delayed the release of citronellol when compared to the untreated cotton. According to a blind evaluation of 30 assessors, the smell of cigarette smoke, trapped onto the fabrics’ surface, was successfully attenuated by porcine odorant binding protein (more than 60 % identified the weakest smell intensity after protein exposure compared to β-cyclodextrin-treated and untreated cotton fabrics). This work demonstrated that porcine odorant binding protein can be an efficient solution to prevent and/orremove unpleasant odours trapped on the large surface of textiles. Its intrinsic properties make odorant binding proteins excellent candidates for controlled release systems which constitute a new application for this class of proteins.This work was co-funded by the European Social Fund through the management authority POPH and FCT. The authors Carla Silva and Teresa Matama would like to acknowledge their post-doctoral fellowships: SFRH/BPD/46515/2008 and SFRH/BPD/47555/2008, respectively

Genetic Signatures of Exceptional Longevity in Humans

Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different “genetic signatures” of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity

Time-series clustering of gene expression in irradiated and bystander fibroblasts: an application of FBPA clustering

<p>Abstract</p> <p>Background</p> <p>The radiation bystander effect is an important component of the overall biological response of tissues and organisms to ionizing radiation, but the signaling mechanisms between irradiated and non-irradiated bystander cells are not fully understood. In this study, we measured a time-series of gene expression after α-particle irradiation and applied the Feature Based Partitioning around medoids Algorithm (FBPA), a new clustering method suitable for sparse time series, to identify signaling modules that act in concert in the response to direct irradiation and bystander signaling. We compared our results with those of an alternate clustering method, Short Time series Expression Miner (STEM).</p> <p>Results</p> <p>While computational evaluations of both clustering results were similar, FBPA provided more biological insight. After irradiation, gene clusters were enriched for signal transduction, cell cycle/cell death and inflammation/immunity processes; but only FBPA separated clusters by function. In bystanders, gene clusters were enriched for cell communication/motility, signal transduction and inflammation processes; but biological functions did not separate as clearly with either clustering method as they did in irradiated samples. Network analysis confirmed p53 and NF-κB transcription factor-regulated gene clusters in irradiated and bystander cells and suggested novel regulators, such as KDM5B/JARID1B (lysine (K)-specific demethylase 5B) and HDACs (histone deacetylases), which could epigenetically coordinate gene expression after irradiation.</p> <p>Conclusions</p> <p>In this study, we have shown that a new time series clustering method, FBPA, can provide new leads to the mechanisms regulating the dynamic cellular response to radiation. The findings implicate epigenetic control of gene expression in addition to transcription factor networks.</p

Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer

Multiconstrained gene clustering based on generalized projections

<p>Abstract</p> <p>Background</p> <p>Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem.</p> <p>Results</p> <p>We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The generalized projector iteratively projects the clustering solution onto these sets in order to find a consistent solution included in the intersection set that satisfies all constraints. Compared with previous MGC methods, POCS can integrate multiple constraints from different nature without distorting the original constraints. To evaluate the clustering solution, we also propose a new performance measure referred to as Gene Log Likelihood (GLL) that considers genes having more than one function and hence in more than one cluster. Comparative experimental results show that our POCS-based gene clustering method outperforms current state-of-the-art MGC methods.</p> <p>Conclusions</p> <p>The POCS-based MGC method can successfully combine multiple constraints from different nature for gene clustering. Also, the proposed GLL is an effective performance measure for the soft clustering solutions.</p

NMR Studies on Structure and Dynamics of the Monomeric Derivative of BS-RNase: New Insights for 3D Domain Swapping

Three-dimensional domain swapping is a common phenomenon in pancreatic-like ribonucleases. In the aggregated state, these proteins acquire new biological functions, including selective cytotoxicity against tumour cells. RNase A is able to dislocate both N- and C-termini, but usually this process requires denaturing conditions. In contrast, bovine seminal ribonuclease (BS-RNase), which is a homo-dimeric protein sharing 80% of sequence identity with RNase A, occurs natively as a mixture of swapped and unswapped isoforms. The presence of two disulfides bridging the subunits, indeed, ensures a dimeric structure also to the unswapped molecule. In vitro, the two BS-RNase isoforms interconvert under physiological conditions. Since the tendency to swap is often related to the instability of the monomeric proteins, in these paper we have analysed in detail the stability in solution of the monomeric derivative of BS-RNase (mBS) by a combination of NMR studies and Molecular Dynamics Simulations. The refinement of NMR structure and relaxation data indicate a close similarity with RNase A, without any evidence of aggregation or partial opening. The high compactness of mBS structure is confirmed also by H/D exchange, urea denaturation, and TEMPOL mapping of the protein surface. The present extensive structural and dynamic investigation of (monomeric) mBS did not show any experimental evidence that could explain the known differences in swapping between BS-RNase and RNase A. Hence, we conclude that the swapping in BS-RNase must be influenced by the distinct features of the dimers, suggesting a prominent role for the interchain disulfide bridges