Enriched protein screening of human bone marrow mesenchymal stromal cell secretions reveals MFAP5 and PENK as novel IL-10 modulators

The secreted proteins from a cell constitute a natural biologic library that can offer significant insight into human health and disease. Discovering new secreted proteins from cells is bounded by the limitations of traditional separation and detection tools to physically fractionate and analyze samples. Here, we present a new method to systematically identify bioactive cell-secreted proteins that circumvent traditional proteomic methods by first enriching for protein candidates by differential gene expression profiling. The bone marrow stromal cell secretome was analyzed using enriched gene expression datasets in combination with potency assay testing. Four proteins expressed by stromal cells with previously unknown anti-inflammatory properties were identified, two of which provided a significant survival benefit to mice challenged with lethal endotoxic shock. Greater than 85% of secreted factors were recaptured that were otherwise undetected by proteomic methods, and remarkable hit rates of 18% in vitro and 9% in vivo were achieved

A time study of physicians' work in a German university eye hospital to estimate unit costs.

BACKGROUND: Technical efficiency of hospital services is debated since performance has been heterogeneous. Staff time represents the main resource in patient care and its inappropriate allocation has been identified as a key factor of inefficiency. The aim of this study was to analyse the utilisation of physicians' work time stratified by staff groups, tasks and places of work. A further aim was to use these data to estimate resource use per unit of output. METHODS: A self-reporting work-sampling study was carried during 14-days at a University Eye Hospital. Staff costs of physicians per unit of output were calculated at the wards, the operating rooms and the outpatient unit. RESULTS: Forty per cent of total work time was spent in contact with the patient. Thirty per cent was spent with documentation tasks. Time spent with documentation tasks declined monotonically with increasing seniority of staff. Unit costs were 56 € per patient day at the wards, 77 € and 20 € per intervention at the operating rooms for inpatients and outpatients, respectively, and 33 € per contact at the outpatient unit. Substantial differences in resources directly dedicated to the patient were found between these locations. CONCLUSION: The presented data provide unprecedented units costs in inpatient Ophthalmology. Future research should focus on analysing factors that influence differences in time allocation, such as types of patients, organisation of care processes and composition of staff

Improving protein function prediction methods with integrated literature data

<p>Abstract</p> <p>Background</p> <p>Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity.</p> <p>Results</p> <p>We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder, but co-occurrence data still proves beneficial.</p> <p>Conclusion</p> <p>Co-occurrence data is a valuable supplemental source for graph-theoretic function prediction algorithms. A rapidly growing literature corpus ensures that co-occurrence data is a readily-available resource for nearly every studied organism, particularly those with small protein interaction databases. Though arguably biased toward known genes, co-occurrence data provides critical additional links to well-studied regions in the interaction network that graph-theoretic function prediction algorithms can exploit.</p

Improving protein function prediction methods with integrated literature data-2

Ted correctly (TP), for FP up to 100. Abbreviations as in Figure 2.<p><b>Copyright information:</b></p><p>Taken from "Improving protein function prediction methods with integrated literature data"</p><p>http://www.biomedcentral.com/1471-2105/9/198</p><p>BMC Bioinformatics 2008;9():198-198.</p><p>Published online 15 Apr 2008</p><p>PMCID:PMC2375131.</p><p></p

Improving protein function prediction methods with integrated literature data-4

Ine the co-occurrence interaction set. Shown is the number of true positives (TP) when the scoring threshold is set to yield 100 false positives (FP) (y axis). The values of the x-axis denote instances of Functional Flow on graphs combining PPI and the interaction sets for each corresponding setting of the co-occurrence threshold (x = -1 shows PPI ONLY and x = 0–9 denote PPI plus the datasets obtained using thresholds 0.0 to 0.9). The lines are annotated to denote the MUT, HYG and ACF metrics. The best and worst performers respectively, over all co-occurrence measure and all thresholds, are shown in parentheses below the plot title. These combinations appear as Best and Worst in Figures 2 and 3.<p><b>Copyright information:</b></p><p>Taken from "Improving protein function prediction methods with integrated literature data"</p><p>http://www.biomedcentral.com/1471-2105/9/198</p><p>BMC Bioinformatics 2008;9():198-198.</p><p>Published online 15 Apr 2008</p><p>PMCID:PMC2375131.</p><p></p

Improving protein function prediction methods with integrated literature data-1

Ted correctly (TP). Abbreviations: GOMF – GO SLIM Molecular Function; GOBP – GO SLIM Biological Process; PPI ONLY – only edges from experiments measuring protein-protein interactions, such as yeast two-hybrid and affinity precipitation; GENETIC ONLY – only edges from genetic assays, such as synthetic lethality studies; PPI+GENETIC – edges from both PPI and from genetic assays, such as synthetic lethality studies; PPI+COLIT – edges from both PPI and edges between proteins found by literature co-occurrence, where Best and Worst correspond to the best and worst combinations of threshold setting and co-occurrence measure, respectively (. Figure 5).<p><b>Copyright information:</b></p><p>Taken from "Improving protein function prediction methods with integrated literature data"</p><p>http://www.biomedcentral.com/1471-2105/9/198</p><p>BMC Bioinformatics 2008;9():198-198.</p><p>Published online 15 Apr 2008</p><p>PMCID:PMC2375131.</p><p></p

Improving protein function prediction methods with integrated literature data-0

O-occurrence measures. Abbreviations: MUT – Mutual Information Measure; HYG – Hypergeometric Measure; ACF – Asymmetric Co-occurrence Fraction.<p><b>Copyright information:</b></p><p>Taken from "Improving protein function prediction methods with integrated literature data"</p><p>http://www.biomedcentral.com/1471-2105/9/198</p><p>BMC Bioinformatics 2008;9():198-198.</p><p>Published online 15 Apr 2008</p><p>PMCID:PMC2375131.</p><p></p