327. Assessment of Bacterial Co-infection Rates and Antibiotic Exposure in COVID-19 Patients

Background

COVID-19 pandemic data suggest risk for bacterial co-infection upon hospital presentation remain extremely low. Despite low co-infection rates, antibiotics are prescribed for most patients. Current data are limited regarding institutional-specific change in antibiotic use over the course of the pandemic. Given the low rates of co-infections, Saint Luke’s Health System’s COVID-19 Treatment Taskforce developed a COVID-19 evaluation and treatment order set which included procalcitonin (PCT) . As co-infection literature emerged, active education was provided, and order sets were modified to provide passive education regarding co-infection rates. We aimed to assess antibiotic practice changes as data and strategies to influence use evolved during the pandemic.

Methods

This was a multi-center, single health-system retrospective cohort study. Ten community hospitals and 1 academic medical center were included in analysis. Inclusion criteria were age ≥18 years, admitted during April or September 2020 and had a positive COVID-19 result on admission. Patients were excluded if they were readmitted for COVID-19 related issues. Both primary and secondary outcomes were analyzed from the first 7 days after admission. The primary outcome was rate of respiratory bacterial co-infections. This was determined through sputum and blood cultures, urinary antigens including Streptococcus pneumoniae and Legionella, and PCT. Secondary outcomes included rate of antibiotic use, antibiotic days of therapy (DOT), length of therapy, and antibiotic use trends.

Baseline Characteristics

Results

A total of 294 patients were included with 69 patients in April 2020 and 225 in September 2020. Primary and secondary results are shown in Table 2. Rate of culture-confirmed bacterial co-infection when examining April 2020 was 4.38% and 4.44 % in September 2020. Antibiotic uses, antibiotic DOT, and length of therapy were all significantly lower in September 2020 compared to April 2020.

Conclusion

Our results show bacterial co-infections were extremely low in our health system. Despite positive trends in antibiotic use, prescribing remained high. More targeted interventions to decrease antibiotic exposure in COVID-19 patients are needed.

Disclosures

All Authors: No reported disclosures

46. Templated Microbiology Comments with Candiduria to Enhance Antimicrobial Stewardship

Background

Behavioral interventions have been shown to improve antimicrobial selection. Such practices are low cost and effective means of stewardship promotion. One area of overtreatment that contributes to unnecessary antifungal use is in hospitalized patients with candiduria. We implemented a templated microbiology comment to guide prescribing of antifungals for hospitalized patients with candiduria.

Methods

This was a quasi-experimental, multi-center, single health system study. When Candida is isolated, the following comment appears in the microbiology result section along with the urine culture result: “In the absence of symptoms, Candida is generally considered normal flora. No therapy indicated unless high risk (pregnant, neonate or neutropenic) or undergoing urologic procedure. If Foley catheter present, remove or replace when able.” We compared a pre-implementation cohort (June 2018-Janurary 2019) to a post-implementation cohort (June 2019-Janurary 2020). Patients were included in the study if they were inpatients, 18 years and older, with candiduria. The primary outcome was the rate of antifungal administration within 72 hours after culture results became available. Secondary outcomes include duration of therapy and rate of antifungal given within 73-240 hours after culture result.

Results

The study included a total of 297 patients between the two groups (156 pre-implementation, 141 post-implementation). The primary outcome was found to be significantly lower in the post-implementation group (48.1% vs 34.0%, p=0.014). A multivariate adjustment for baseline characteristics that were significantly different between groups revealed that post-implementation group maintained its effect (OR 0.49 (0.29, 0.82), p=0.0067). For secondary outcomes, no difference was found in patients requiring antifungal administration within 73-240 hours after microbiology results were available (1.3% vs 3.5%, p=0.199). There was no difference in mean antifungal duration (4 vs 3 days, p=0.449).

Conclusion

Adding a templated comment to urine cultures was associated with a significant reduction in the number of antifungals prescribed in patients with candiduria. This strategy is an effective low-cost, passive education technique to improve antimicrobial stewardship.

Disclosures

All Authors: No reported disclosures

266. Ceftriaxone Versus Cefazolin for the Treatment of Methicillin-Susceptible Staphylococcus aureus Bacteremia

Background

Few studies have evaluated the use of ceftriaxone (CRO) in the treatment of Methicillin-sensitive Staphylococcus aureus (MSSA) infections. Available studies include a small number of patients with MSSA bacteremia, with conflicting results and several limitations. The purpose of this study was to compare the safety and efficacy of CRO versus cefazolin (CFZ) for patients with MSSA bacteremia.

Methods

This was a multi-center, single health-system retrospective cohort study. Patients were included if they were at least 18 years old, had a primary episode of MSSA bacteremia within Saint Luke’s Health System and received CRO or CEFZ as definitive therapy for MSSA bacteremia. Patients were excluded if they had a previous MSSA bacteremia within 6 months, a polymicrobial infection, received combination antimicrobial therapy as definitive therapy, started treatment at outside hospital, treated for less than 72 hours, or deemed palliative or comfort care. The primary endpoint was clinical cure at 7, 10, 14, and 28 days, or discharge, whichever came first. Secondary endpoints included time to clinical cure or discharge, treatment failure at 90 days, time to treatment failure, readmission due to recurrent MSSA bacteremia at 30 and 90 days, duration of bacteremia, discontinuation of definitive treatment due to adverse drug events, incidence of Clostridiodes difficile infection, and hospital length of stay.

Results

A total of 248 patients met inclusion criteria. Among these, 87 (35.1%) received CRO and 161 (64.9%) received CFZ as definitive therapy. Patient baseline and treatment characteristics are shown in Table 1. The primary outcome occurred in 75 (86.2%) patients in the CRO group vs 145 (90.1%) patients in the CFZ group (P= 0.359), even after adjusting for Charlson Comorbidity Index, Pitt bacteremia score and serum creatinine, (aOR=0.74, 95% CI 0.32 – 1.72; p=0.473). There were no differences in time to clinical cure or discharge, treatment failure at 90 days, or safety events between the two groups. Primary and secondary endpoints are included in Table 2.

Table 1

Table 2

Conclusion

Our study suggests that there is no clinical difference between CRO and CFZ for the treatment of MSSA bacteremia. Further studies are needed to confirm these findings.

Disclosures

All Authors: No reported disclosures

Novel Methods of Incorporating Time in Longitudinal Multivariate Analysis Reveals Hidden Associations With Disease Activity in Systemic Lupus Erythematosus

Objective: Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease. SLE is characterized by high inter-patient variability, including fluctuations over time, a factor which most biomarker studies omit from consideration. We investigated relationships between disease activity and biomarker expression in SLE, using novel methods to control for time-dependent variability, in a proof-of-concept study to evaluate whether doing so revealed additional information.

Methods: We measured 4 serum biomarkers (MIF, CCL2, CCL19, and CXCL10) and 13 routine clinical laboratory parameters, alongside disease activity measured by the SLE disease activity index-2k (SLEDAI-2k), collected longitudinally. We analyzed these data with unsupervised learning methods via ensemble clustering, incorporating temporal relationships using dynamic time warping for distance metric calculation.

Results: Data from 843 visits in 110 patients (median age 47, 83% female) demonstrated highly heterogeneous time-dependent relationships between disease activity and biomarkers. Unbiased magnitude-based hierarchical clustering of biomarker expression levels isolated a patient subset (n = 9) with distinctively heterogeneous expression of the 17 biological parameters, and who had MIF, CCL2, CCL19, and CXCL10 levels that were higher and more strongly associated with disease activity, based on leave-one-out cross-validated regression analysis. In the remaining subgroup, a time-dependent regression model revealed significantly stronger predictive power of biomarkers for disease activity, compared to a time-agnostic regression model. Despite no significant difference in simple magnitude, using dynamic time warping analysis to align longitudinal profiles revealed a large subset (n = 69) with significantly stronger associations between biological parameters and disease activity. This subgroup had significantly lower flare rates, disease activity and damage scores, suggesting this clustering is clinically meaningful.

Conclusions: These results suggest associations between biological parameters and disease activity in SLE exist in a multi-dimensional time-dependent pattern, with implications for the analysis of biomarkers in SLE often used to identify therapeutic targets. Novel methods to analyse high-dimensional data and control for time-dependent variability may have broad utility in the study complex relationships between clinical and biological parameters.

Combinatorial Ranking of Gene Sets to Predict Disease Relapse: The Retinoic Acid Pathway in Early Prostate Cancer

Background

Quantitative high-throughput data deposited in consortia such as International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) present opportunities and challenges for computational analyses.

Methods

We present a computational strategy to systematically rank and investigate a large number (2¹⁰–2²⁰) of clinically testable gene sets, using combinatorial gene subset generation and disease-free survival (DFS) analyses. This approach integrates protein–protein interaction networks, gene expression, DNA methylation, and copy number data, in association with DFS profiles from patient clinical records.

Results

As a case study, we applied this pipeline to systematically analyze the role of ALDH1A2 in prostate cancer (PCa). We have previously found this gene to have multiple roles in disease and homeostasis, and here we investigate the role of the associated ALDH1A2 gene/protein networks in PCa, using our methodology in combination with PCa patient clinical profiles from ICGC and TCGA databases. Relationships between gene signatures and relapse were analyzed using Kaplan–Meier (KM) log-rank analysis and multivariable Cox regression. Relative expression versus pooled mean from diploid population was used for z-statistics calculation. Gene/protein interaction network analyses generated 11 core genes associated with ALDH1A2; combinatorial ranking of the power set of these core genes identified two gene sets (out of 2¹¹ − 1 = 2,047 combinations) with significant correlation with disease relapse (KM log rank p < 0.05). For the more significant of these two sets, referred to as the optimal gene set (OGS), patients have median survival 62.7 months with OGS alterations compared to >150 months without OGS alterations (p = 0.0248, hazard ratio = 2.213, 95% confidence interval = 1.1–4.098). Two genes comprising OGS (CYP26A1 and RDH10) are strongly associated with ALDH1A2 in the retinoic acid (RA) pathways, suggesting a major role of RA signaling in early PCa progression. Our pipeline complements human expertise in the search for prognostic biomarkers in large-scale datasets.

View from the heart: cardiac fibroblasts in development, scarring and regeneration

In the adult, tissue repair after injury is generally compromised by fibrosis, which maintains tissue integrity with scar formation but does not restore normal architecture and function. The process of regeneration is necessary to replace the scar and rebuild normal functioning tissue. Here, we address this problem in the context of heart disease, and discuss the origins and characteristics of cardiac fibroblasts, as well as the crucial role that they play in cardiac development and disease. We discuss the dual nature of cardiac fibroblasts, which can lead to scarring, pathological remodelling and functional deficit, but can also promote heart function in some contexts. Finally, we review current and proposed approaches whereby regeneration could be fostered by interventions that limit scar formation.

CARFMAP: A Curated Pathway Map of Cardiac Fibroblasts

The adult mammalian heart contains multiple cell types that work in unison under tightly regulated conditions to maintain homeostasis. Cardiac fibroblasts are a significant and unique population of non-muscle cells in the heart that have recently gained substantial interest in the cardiac biology community. To better understand this renaissance cell, it is essential to systematically survey what has been known in the literature about the cellular and molecular processes involved. We have built CARFMAP (http://visionet.erc.monash.edu.au/CARFMAP), an interactive cardiac fibroblast pathway map derived from the biomedical literature using a software-assisted manual data collection approach. CARFMAP is an information-rich interactive tool that enables cardiac biologists to explore the large body of literature in various creative ways. There is surprisingly little overlap between the cardiac fibroblast pathway map, a foreskin fibroblast pathway map, and a whole mouse organism signalling pathway map from the REACTOME database. Among the use cases of CARFMAP is a common task in our cardiac biology laboratory of identifying new genes that are (1) relevant to cardiac literature, and (2) differentially regulated in high-throughput assays. From the expression profiles of mouse cardiac and tail fibroblasts, we employed CARFMAP to characterise cardiac fibroblast pathways. Using CARFMAP in conjunction with transcriptomic data, we generated a stringent list of six genes that would not have been singled out using bioinformatics analyses alone. Experimental validation showed that five genes (Mmp3, Il6, Edn1, Pdgfc and Fgf10) are differentially regulated in the cardiac fibroblast. CARFMAP is a powerful tool for systems analyses of cardiac fibroblasts, facilitating systems-level cardiovascular research.

VISIONET: intuitive visualisation of overlapping transcription factor networks, with applications in cardiogenic gene discovery

BACKGROUND

Existing de novo software platforms have largely overlooked a valuable resource, the expertise of the intended biologist users. Typical data representations such as long gene lists, or highly dense and overlapping transcription factor networks often hinder biologists from relating these results to their expertise.

RESULTS

VISIONET, a streamlined visualisation tool built from experimental needs, enables biologists to transform large and dense overlapping transcription factor networks into sparse human-readable graphs via numerically filtering. The VISIONET interface allows users without a computing background to interactively explore and filter their data, and empowers them to apply their specialist knowledge on far more complex and substantial data sets than is currently possible. Applying VISIONET to the Tbx20-Gata4 transcription factor network led to the discovery and validation of Aldh1a2, an essential developmental gene associated with various important cardiac disorders, as a healthy adult cardiac fibroblast gene co-regulated by cardiogenic transcription factors Gata4 and Tbx20.

CONCLUSIONS

We demonstrate with experimental validations the utility of VISIONET for expertise-driven gene discovery that opens new experimental directions that would not otherwise have been identified.

Systems approaches in integrative cardiac biology: illustrations from cardiac heterocellular signalling studies

Understanding the complexity of cardiac physiology requires system-level studies of multiple cardiac cell types. Frequently, however, the end result of published research lacks the detail of the collaborative and integrative experimental design process, and the underlying conceptual framework. We review the recent progress in systems modelling and omics analysis of the heterocellular heart environment through complementary forward and inverse approaches, illustrating these conceptual and experimental frameworks with case studies from our own research program. The forward approach begins by collecting curated information from the niche cardiac biology literature, and connecting the dots to form mechanistic network models that generate testable system-level predictions. The inverse approach starts from the vast pool of public omics data in recent cardiac biological research, and applies bioinformatics analysis to produce novel candidates for further investigation. We also discuss the possibility of combining these two approaches into a hybrid framework, together with the benefits and challenges. These interdisciplinary research frameworks illustrate the interplay between computational models, omics analysis, and wet lab experiments, which holds the key to making real progress in improving human cardiac wellbeing.

Molecular composition of recycled organic wastes, as determined by solid-state 13C NMR and elemental analyses

Using solid state (13)C NMR data and elemental composition in a molecular mixing model, we estimated the molecular components of the organic matter in 16 recycled organic (RO) wastes representative of the major materials generated in the Sydney basin area. Close correspondence was found between the measured NMR signal intensities and those predicted by the model for all RO wastes except for poultry manure char. Molecular nature of the organic matter differed widely between the RO wastes. As a proportion of organic C, carbohydrate C ranged from 0.07 to 0.63, protein C from <0.01 to 0.66, lignin C from <0.01 to 0.31, aliphatic C from 0.09 to 0.73, carbonyl C from 0.02 to 0.23, and char C from 0 to 0.45. This method is considered preferable to techniques involving imprecise extraction methods for RO wastes. Molecular composition data has great potential as a predictor of RO waste soil carbon and nutrient outcomes.

A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

Background

Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers.

Results

We compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/.

Conclusions

The examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be their union, according to three out of four ranking criteria. We propose as a general rule of thumb that the union of classifiers will be close to optimal.

Software support for SBGN maps: SBGN-ML and LibSBGN

Motivation: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.

Availability and implementation: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net.

Contact:sbgn-libsbgn@lists.sourceforge.net

Computational characterization of 3' splice variants in the GFAP isoform family

Glial fibrillary acidic protein (GFAP) is an intermediate filament (IF) protein specific to central nervous system (CNS) astrocytes. It has been the subject of intense interest due to its association with neurodegenerative diseases, and because of growing evidence that IF proteins not only modulate cellular structure, but also cellular function. Moreover, GFAP has a family of splicing isoforms apparently more complex than that of other CNS IF proteins, consistent with it possessing a range of functional and structural roles. The gene consists of 9 exons, and to date all isoforms associated with 3' end splicing have been identified from modifications within intron 7, resulting in the generation of exon 7a (GFAPδ/ε) and 7b (GFAPκ). To better understand the nature and functional significance of variation in this region, we used a Bayesian multiple change-point approach to identify conserved regions. This is the first successful application of this method to a single gene--it has previously only been used in whole-genome analyses. We identified several highly or moderately conserved regions throughout the intron 7/7a/7b regions, including untranslated regions and regulatory features, consistent with the biology of GFAP. Several putative unconfirmed features were also identified, including a possible new isoform. We then integrated multiple computational analyses on both the DNA and protein sequences from the mouse, rat and human, showing that the major isoform, GFAPα, has highly conserved structure and features across the three species, whereas the minor isoforms GFAPδ/ε and GFAPκ have low conservation of structure and features at the distal 3' end, both relative to each other and relative to GFAPα. The overall picture suggests distinct and tightly regulated functions for the 3' end isoforms, consistent with complex astrocyte biology. The results illustrate a computational approach for characterising splicing isoform families, using both DNA and protein sequences.

Bioinformatic approaches for predicting substrates of proteases

Proteases have central roles in "life and death" processes due to their important ability to catalytically hydrolyze protein substrates, usually altering the function and/or activity of the target in the process. Knowledge of the substrate specificity of a protease should, in theory, dramatically improve the ability to predict target protein substrates. However, experimental identification and characterization of protease substrates is often difficult and time-consuming. Thus solving the "substrate identification" problem is fundamental to both understanding protease biology and the development of therapeutics that target specific protease-regulated pathways. In this context, bioinformatic prediction of protease substrates may provide useful and experimentally testable information about novel potential cleavage sites in candidate substrates. In this article, we provide an overview of recent advances in developing bioinformatic approaches for predicting protease substrate cleavage sites and identifying novel putative substrates. We discuss the advantages and drawbacks of the current methods and detail how more accurate models can be built by deriving multiple sequence and structural features of substrates. We also provide some suggestions about how future studies might further improve the accuracy of protease substrate specificity prediction.

Probing connectivity between transcriptional and post-transcriptional gene networks

The foundation for much of our current understanding of eukaryotic cell biology stems from studies exploiting the combined power of this yeast?s genetic tractability and its simple growth requirements. Furthermore, access to an early complete genome in 1996 allowed yeast researchers to spearhead the move toward genome-wide studies that underpin our thinking about systems-level biology today. Indeed, the last decade has been so rich in these studies that it has become close to impossible for most biologists to interrogate the data in an unbiased fashion. The challenge for the next decade is to generate the informational tools to sort the multidimensional datasets for underlying networks.

A novel Entamoeba histolytica cysteine proteinase, EhCP4, is key for invasive amebiasis and a therapeutic target

Entamoeba histolytica cysteine proteinases (EhCPs) play a key role in disrupting the colonic epithelial barrier and the innate host immune response during invasion of E. histolytica, the protozoan cause of human amebiasis. EhCPs are encoded by 50 genes, of which ehcp4 (ehcp-a4) is the most up-regulated during invasion and colonization in a mouse cecal model of amebiasis. Up-regulation of ehcp4 in vivo correlated with our finding that co-culture of E. histolytica trophozoites with mucin-producing T84 cells increased ehcp4 expression up to 6-fold. We have expressed recombinant EhCP4, which was autocatalytically activated at acidic pH but had highest proteolytic activity at neutral pH. In contrast to the other amebic cysteine proteinases characterized so far, which have a preference for arginine in the P2 position, EhCP4 displayed a unique preference for valine and isoleucine at P2. This preference was confirmed by homology modeling, which revealed a shallow, hydrophobic S2 pocket. Endogenous EhCP4 localized to cytoplasmic vesicles, the nuclear region, and perinuclear endoplasmic reticulum (ER). Following co-culture with colonic cells, EhCP4 appeared in acidic vesicles and was released extracellularly. A specific vinyl sulfone inhibitor, WRR605, synthesized based on the substrate specificity of EhCP4, inhibited the recombinant enzyme in vitro and significantly reduced parasite burden and inflammation in the mouse cecal model. The unique expression pattern, localization, and biochemical properties of EhCP4 could be exploited as a potential target for drug design.

Cascleave: towards more accurate prediction of caspase substrate cleavage sites

MOTIVATION

The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates.

RESULTS

In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P(1) Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship.

AVAILABILITY

http://sunflower.kuicr.kyoto-u.ac.jp/ approximately sjn/Cascleave/

CONTACT

jiangning.song@med.monash.edu.au; takutsu@kuicr.kyoto-u.ac.jp; james; whisstock@med.monash.edu.au

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Characterization of a serine protease homologous to house dust mite group 3 allergens from the scabies mite Sarcoptes scabiei

The scabies mite, Sarcoptes scabiei var. hominis, infests human skin, causing allergic reactions and facilitating bacterial infection by Streptococcus sp., with serious consequences such as rheumatic fever and rheumatic heart disease. To identify a possible drug target or vaccine candidate protein, we searched for homologues of the group 3 allergen of house dust mites, which we subsequently identified in a cDNA library. The native protein, designated Sar s 3, was shown to be present in the mite gut and excreted in fecal pellets into mite burrows within the upper epidermis. The substrate specificity of proteolytically active recombinant rSar s 3 was elucidated by screening a bacteriophage library. A preference for substrates containing a RS(G/A) sequence at the P1-P2' positions was revealed. A series of peptides synthesized as internally quenched fluorescent substrates validated the phage display data and high performance liquid chromatography/mass spectrometry analysis of the preferred cleaved substrate and confirmed the predicted cleavage site. Searches of the human proteome using sequence data from the phage display allowed the in silico prediction of putative physiological substrates. Among these were numerous epidermal proteins, with filaggrin being a particularly likely candidate substrate. We showed that recombinant rSar s 3 cleaves human filaggrin in vitro and obtained immunohistological evidence that the filaggrin protein is ingested by the mite. This is the first report elucidating the substrate specificity of Sar s 3 and its potential role in scabies mite biology.

The Systems Biology Graphical Notation

Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.

Cooperative effects in the substrate specificity of the complement protease C1s

Complement is a key component of the immune system, but can contribute to inflammatory diseases. The substrate specificity of C1s protease has been successfully investigated using a combinatorial approach, while a positional scanning method failed. The lack of success of the latter approach is possibly due to cooperativity in the active site, which could confound such analyses. With a panel of peptides devised using factorial design, we show pronounced cooperativity between the S4 and S1′ subsites in the active site of the enzyme, and weaker cooperativity between the S1′ and S3′ subsites. The use of factorial design has promise as a methodology for determining cooperativity in protease active sites.

Subsite cooperativity in protease specificity

Proteases play vital roles in a range of biological processes, such as cell cycle, cell growth and differentiation, apoptosis, haemostasis and signalling. Fundamental to our knowledge of protease action is an understanding of how the active site operates; this has been examined through extensive studies of the substrate specificity of the enzymes. Kinetic and structural analyses have shown that the binding of a particular substrate residue at a protease subsite can have either a positive or negative influence on the binding of particular residues at other subsites. This phenomenon has been termed subsite cooperativity and has been observed in a wide range of proteases, often between non-adjacent subsites. This review aims to highlight studies where subsite cooperativity has been observed, experimental techniques used in the past and potential methods that can be employed to comprehensively examine this phenomenon. Further understanding of how the protease active site recognises and chooses its substrates for cleavage will have a significant impact on the development of pharmaceuticals that target these enzymes.

Caspase-8 cleaves histone deacetylase 7 and abolishes its transcription repressor function

Caspase-8 is the initiator caspase of the extrinsic apoptosis pathway and also has a role in non-apoptotic physiologies. Identifying endogenous substrates for caspase-8 by using integrated bioinformatics and biological approaches is required to delineate the diverse roles of this caspase. We describe a number of novel putative caspase-8 substrates using the Prediction of Protease Specificity (PoPS) program, one of which is histone deacetylase 7 (HDAC7). HDAC7 is cleaved faster than any other caspase-8 substrate described to date. It is also cleaved in primary CD4+CD8+ thymocytes undergoing extrinsic apoptosis. By using naturally occurring caspase inhibitors that have evolved exquisite specificity at concentrations found within the cell, we could unequivocally assign the cleavage activity to caspase-8. Importantly, cleavage of HDAC7 alters its subcellular localization and abrogates its Nur77 repressor function. Thus we demonstrate a direct role for initiator caspase-mediated proteolysis in promoting gene transcription.

Elucidation of the substrate specificity of the MASP-2 protease of the lectin complement pathway and identification of the enzyme as a major physiological target of the serpin, C1-inhibitor

Complement is a central component of host defence, but unregulated activation can contribute to disease. The system can be initiated by three pathways: classical, alternative and lectin. The classical and lectin pathways are initiated by the C1 and mannose-binding lectin (MBL) or ficolin complexes, respectively, with C1s the executioner protease of the C1 complex and MASP-2 its counterpart in the lectin complexes. These proteases in turn cleave the C4 and C2 components of the system. Here we have elucidated the cleavage specificity of MASP-2 using a randomised substrate phage display library. Apart from the crucial P1 position, the MASP-2 S2 and S3 subsites (in that order) play the greatest role in determining specificity, with Gly residues preferred at P2 and Leu or hydrophobic residues at P3. Cleavage of peptide substrates representing the known physiological cleavage sequences in C2, C4 or the serpin C1-inhibitor (a likely regulator of MASP-2) revealed that MASP-2 is up to 1000 times more catalytically active than C1s. C1-inhibitor inhibited MASP-2 50-fold faster than C1s and much faster than any other protease tested to date, implying that MASP-2 is a major physiological target of C1-inhibitor.

The protease allergen Der p 1 cleaves cell surface DC-SIGN and DC-SIGNR: experimental analysis of in silico substrate identification and implications in allergic responses

BACKGROUND

The cysteine protease Der p 1 from the house dust mite Dermatophagoides pteronyssinus is one of the most potent allergens known. An attractive mechanism for a component of Der p 1 allergenicity lies in its ability to cleave key regulatory molecules from leucocyte surfaces, subverting cellular function and driving abnormal immunoglobulin E (IgE) responses.

OBJECTIVE

Although CD23, CD25 and CD40 have already been identified as major Der p 1 targets, other significant substrates may also exist.

METHODS

To investigate this, knowledge of the proteolytic properties of Der p 1 was used to perform in silico digestion of human dendritic cell surface proteins, using the prediction of protease specificity (PoPS) bioinformatics tool, in conjunction with cellular in vitro analysis and cleavage site determination.

RESULTS

Targets identified included DC-SIGN and DC-SIGNR, two C-type lectins implicated mostly in pathogen trafficking. Treatment of positively expressing cells with Der p 1 led to loss of detectable surface DC-SIGN and DC-SIGNR. Digestion of purified soluble recombinant DC-SIGN and DC-SIGNR, followed by N-terminal sequencing and MALDI mass spectrometry, indicated in each case one major cleavage site and several minor sites, the former correlating well with Der p 1 enzymology and the folded state of the substrate proteins. Loss of DC-SIGN from the cell surface led to reduced binding of intracellular adhesion molecule-3, an endogenous DC-SIGN ligand expressed on naïve T cells which is thought to be involved in T-helper type 1 cytokine signalling.

CONCLUSION

These data provide evidence of lectin involvement in the initiation of the allergic response and the value of using genome-wide in silico digestion tools.

PoPS: a computational tool for modeling and predicting protease specificity

Proteases play a fundamental role in the control of intra- and extracellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://pops.csse.monash.edu.au/) which provides a novel method for building computational models of protease specificity that, while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.

Management of Staphylococcus aureus infections

Because of high incidence, morbidity, and antimicrobial resistance, Staphylococcus aureus infections are a growing concern for family physicians. Strains of S. aureus that are resistant to vancomycin are now recognized. Increasing incidence of unrecognized community-acquired methicillin-resistant S. aureus infections pose a high risk for morbidity and mortality. Although the incidence of complex S. aureus infections is rising, new antimicrobial agents, including daptomycin and linezolid, are available as treatment. S. aureus is a common pathogen in skin, soft-tissue, catheter-related, bone, joint, pulmonary, and central nervous system infections. S. aureus bacteremias are particularly problematic because of the high incidence of associated complicated infections, including infective endocarditis. Adherence to precautions recommended by the Centers for Disease Control and Prevention, especially handwashing, is suboptimal.

PoPS: a computational tool for modeling and predicting protease specificity

Proteases play a fundamental role in the control of intra- and extra-cellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://pops.csse.monash.edu.au/) that provides a novel method for building computational models of protease specificity, which while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.

Synthesis and Structure of Diacetato-[1-benzyloxy-1,1-bis(N-methylimdazol-2-yl)ethane]zinc(II)

The synthesis and single-crystal X-ray structure determination of the title compound are described. The crystals are monoclinic, P21/c, a 10.064(2), b 13.096(2), c 17.722(3) Ǻ, β 104.68(2)°; the structure was refined to a residual of 0.033 for 3138 independent observed reflections. The coordination about zinc is intermediate between four- and five-coordinate, and the angle between the planar imidazole groups is 14.9°.