Comparable clinical course between coagulase-negative staphylococcal and Staphylococcus aureus endocarditis.

PURPOSE: Staphylococcus aureus (SA) is involved in almost one-third of endocarditis events (known as E-SA) and is frequently associated with unfavorable outcomes compared to infectious endocarditis (IE) caused by other pathogens including coagulase-negative staphylococci (CNS). The aim of this study was to compare the morbidity and mortality of patients with E-SA and endocarditis due to CNS (known as E-CNS). METHODS: A monocentric retrospective cohort analysis was conducted including all patients admitted with IE from January 2010 to December 2017. Lengths of stay, complications, in-hospital and 1-year mortality were described from medical records and compared between E-SA and E-CNS. RESULTS: Among the 428 patients included, 102 had staphylococcus (50 E-SA and 52 E-CNS). Half of the IE events due to staphylococcus occurred in the year following a cardiac procedure [p = 0.029]. A septic embolism occurred in 41% and 48% of patients with E-CNS and E-SA, respectively [p = 0.439]. Cardiac surgery was indicated in 50% of E-SA and 48% of E-CNS cases [p = 0.846]. The intra-hospital and 1-year mortality rates were 25% and 31% for E-CNS and 34% and 45% for E-SA [p = 0.699, p = 0.234]. CONCLUSION: Embolic complications, surgical management rate and mortality rates of E-SA and E-CNS were comparable, which may suggest a similar morbidity and mortality irrespective of the pathogen involved in IE.

Osteosynthesis-associated infection of the lower limbs by multidrug-resistant and extensively drug-resistant Gram-negative bacteria: a multicentre cohort study.

Purpose: The purpose of this study was the clinical and therapeutic assessment of lower-limb osteosynthesis-associated infection (OAI) by multidrug-resistant (MDR) and extensively drug-resistant (XDR) Gram-negative bacteria (GNB), which have been poorly studied to date. Methods: A prospective multicentre observational study was conducted on behalf of ESGIAI (the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group on Implant-Associated Infections). Factors associated with remission of the infection were evaluated by multivariate and Cox regression analysis for a 24-month follow-up period. Results: Patients ( n = 57 ) had a history of trauma (87.7 %), tumour resection (7 %) and other bone lesions (5.3 %). Pathogens included Escherichia coli ( n = 16 ), Pseudomonas aeruginosa ( n = 14 ; XDR 50 %), Klebsiella spp. ( n = 7 ), Enterobacter spp. ( n = 9 ), Acinetobacter spp. ( n = 5 ), Proteus mirabilis ( n = 3 ), Serratia marcescens ( n = 2 ) and Stenotrophomonas maltophilia ( n = 1 ). The prevalence of ESBL (extended-spectrum ß -lactamase), fluoroquinolone and carbapenem resistance were 71.9 %, 59.6 % and 17.5 % respectively. Most patients ( n = 37 ; 64.9 %) were treated with a combination including carbapenems ( n = 32 ) and colistin ( n = 11 ) for a mean of 63.3 d. Implant retention with debridement occurred in early OAI (66.7 %), whereas the infected device was removed in late OAI (70.4 %) ( p = 0.008 ). OAI remission was achieved in 29 cases (50.9 %). The type of surgery, antimicrobial resistance and duration of treatment did not significantly influence the outcome. Independent predictors of the failure to eradicate OAI were age > 60  years (hazard ratio, HR, of 3.875; 95 % confidence interval, CI95 %, of 1.540-9.752; p = 0.004 ) and multiple surgeries for OAI (HR of 2.822; CI95 % of 1.144-6.963; p = 0.024 ). Conclusions: Only half of the MDR/XDR GNB OAI cases treated by antimicrobials and surgery had a successful outcome. Advanced age and multiple surgeries hampered the eradication of OAI. Optimal therapeutic options remain a challenge.

Conceptual and computational framework for logical modelling of biological networks deregulated in diseases.

Mathematical models can serve as a tool to formalize biological knowledge from diverse sources, to investigate biological questions in a formal way, to test experimental hypotheses, to predict the effect of perturbations and to identify underlying mechanisms. We present a pipeline of computational tools that performs a series of analyses to explore a logical model's properties. A logical model of initiation of the metastatic process in cancer is used as a transversal example. We start by analysing the structure of the interaction network constructed from the literature or existing databases. Next, we show how to translate this network into a mathematical object, specifically a logical model, and how robustness analyses can be applied to it. We explore the visualization of the stable states, defined as specific attractors of the model, and match them to cellular fates or biological read-outs. With the different tools we present here, we explain how to assign to each solution of the model a probability and how to identify genetic interactions using mutant phenotype probabilities. Finally, we connect the model to relevant experimental data: we present how some data analyses can direct the construction of the network, and how the solutions of a mathematical model can also be compared with experimental data, with a particular focus on high-throughput data in cancer biology. A step-by-step tutorial is provided as a Supplementary Material and all models, tools and scripts are provided on an accompanying website: https://github.com/sysbio-curie/Logical_modelling_pipeline.

Multidrug-resistant and extensively drug-resistant Gram-negative prosthetic joint infections: Role of surgery and impact of colistin administration.

Factors influencing treatment outcome of patients with Gram-negative bacterial (GNB) multidrug-resistant (MDR) and extensively drug-resistant (XDR) prosthetic joint infection (PJIs) were analysed. Data were collected (2000-2015) by 18 centres. Treatment success was analysed by surgery type for PJI, resistance (MDR/XDR) and antimicrobials (colistin/non-colistin) using logistic regression and survival analyses. A total of 131 patients (mean age 73.0 years, 35.9% male, 58.8% with co-morbidities) with MDR (n = 108) or XDR (n = 23) GNB PJI were assessed. The most common pathogens were Escherichia coli (33.6%), Pseudomonas aeruginosa (25.2%), Klebsiella pneumoniae (21.4%) and Enterobacter cloacae (17.6%). Pseudomonas aeruginosa predominated in XDR cases. Isolates were carbapenem-resistant (n = 12), fluoroquinolone-resistant (n = 63) and ESBL-producers (n = 94). Treatment outcome was worse in XDR versus MDR cases (P = 0.018). Success rates did not differ for colistin versus non-colistin in XDR cases (P = 0.657), but colistin was less successful in MDR cases (P = 0.018). Debridement, antibiotics and implant retention (DAIR) (n = 67) was associated with higher failure rates versus non-DAIR (n = 64) (OR = 3.57, 95% CI 1.68-7.58; P < 0.001). Superiority of non-DAIR was confirmed by Kaplan-Meir analysis (HR = 0.36, 95% CI 0.20-0.67) and remained unchangeable by time of infection (early/late), antimicrobial resistance (MDR/XDR) and antimicrobials (colistin/non-colistin) (Breslow-Day, P = 0.737). DAIR is associated with higher failure rates even in early MDR/XDR GNB PJIs versus implant removal. Colistin should be preserved for XDR cases as it is detrimental in MDR infections.

Performance comparison of three DNA extraction kits on human whole-exome data from formalin-fixed paraffin-embedded normal and tumor samples.

Next-generation sequencing (NGS) studies are becoming routinely used for the detection of novel and clinically actionable DNA variants at a pangenomic scale. Such analyses are now used in the clinical practice to enable precision medicine. Formalin-fixed paraffin-embedded (FFPE) tissues are still one of the most abundant source of cancer clinical specimen, unfortunately this method of preparation is known to degrade DNA and therefore compromise subsequent analysis. Some studies have reported that variant detection can be performed on FFPE samples sequenced with NGS techniques, but few or none have done an in-depth coverage analysis and compared the influence of different state-of-the-art FFPE DNA extraction kits on the quality of the variant calling. Here, we generated 42 human whole-exome sequencing data sets from fresh-frozen (FF) and FFPE samples. These samples include normal and tumor tissues from two different organs (liver and colon), that we extracted with three different FFPE extraction kits (QIAamp DNA FFPE Tissue kit and GeneRead DNA FFPE kit from Qiagen, Maxwell™ RSC DNA FFPE Kit from Promega). We determined the rate of concordance of called variants between matched FF and FFPE samples on all common variants (representing at least 86% of the total number of variants for SNVs). The concordance rate is very high between all matched FF / FFPE pairs, with equivalent values for the three kits we analyzed. On the other hand, when looking at the difference between the total number of variants in FF and FFPE, we find a significant variation for the three different FFPE DNA extraction kits. Coverage analysis shows that FFPE samples have less good indicators than FF samples, yet the coverage quality remains above accepted thresholds. We detect limited but statistically significant variations in coverage indicator values between the three FFPE extraction kits. Globally, the GeneRead and QIAamp kits have better variant calling and coverage indicators than the Maxwell kit on the samples used in this study, although this kit performs better on some indicators and has advantages in terms of practical usage. Taken together, our results confirm the potential of FFPE samples analysis for clinical genomic studies, but also indicate that the choice of a FFPE DNA extraction kit should be done with careful testing and analysis beforehand in order to maximize the accuracy of the results.

Staphylococcus lugdunensis: a neglected pathogen of infections involving fracture-fixation devices.

PURPOSE: Cases of fracture-fixation device infection involving Staphylococcus lugdunensis are not frequent. The clinical characteristics and the choice of treatment strategies of these infections are not obviously known to date. METHODS: We performed a review of fracture-fixation device infection involving S. lugdunensis managed by our centres. RESULTS: Among the 38 cases of fracture-fixation device infection involving S. lugdunensis, 53% were located in the tibia. Most of our cases (87%) were chronic infections. Purulent discharge, which occurred in 79% of cases, was the most frequent clinical symptom, followed by pain in 63%, local inflammation in 55%, and fever in 37%. Bacteremia and severe sepsis occurred in 10% and 18% of cases, respectively. Four cases (10%) were treated exclusively with antimicrobial treatment alone. Thirty-four cases (89%) were treated with a combination of surgery with antimicrobial therapy including surgical debridement, antibiotics and osteosynthesis device retention in six cases (16%), and osteosynthesis device removal in 27 cases (71%). The mean length of antibiotic treatment was 119 days. The relapse rate was high that was not related to selection of resistant strains. Polymicrobial infection had no impact on clinical outcome. A combination of surgery with antimicrobial therapy was identified as a significant prognostic factor associated with remission (p = 0.042). CONCLUSIONS: S. lugdunensis is probably involved in more infections than has been reported. Using appropriate microbiological methods laboratories should routinely identify the species of all coagulase-negative Staphylococci isolates involved in fracture-fixation device infection to better achieve the treatment strategies of fracture-fixation device infection involving S. lugdunensis.

NaviCell Web Service for network-based data visualization.

Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of 'omics' data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases.

Integrative multi-omics module network inference with Lemon-Tree.

Module network inference is an established statistical method to reconstruct co-expression modules and their upstream regulatory programs from integrated multi-omics datasets measuring the activity levels of various cellular components across different individuals, experimental conditions or time points of a dynamic process. We have developed Lemon-Tree, an open-source, platform-independent, modular, extensible software package implementing state-of-the-art ensemble methods for module network inference. We benchmarked Lemon-Tree using large-scale tumor datasets and showed that Lemon-Tree algorithms compare favorably with state-of-the-art module network inference software. We also analyzed a large dataset of somatic copy-number alterations and gene expression levels measured in glioblastoma samples from The Cancer Genome Atlas and found that Lemon-Tree correctly identifies known glioblastoma oncogenes and tumor suppressors as master regulators in the inferred module network. Novel candidate driver genes predicted by Lemon-Tree were validated using tumor pathway and survival analyses. Lemon-Tree is available from http://lemon-tree.googlecode.com under the GNU General Public License version 2.0.

[Biological network modelling and precision medicine in oncology]. / Modélisation de réseaux biologiques et médecine de précision en oncologie.

Precision medicine in oncology is becoming reality thanks to the next-generation sequencing of tumours and the development of targeted inhibitors enabling tailored therapies. Many clinical trials base their strategy on the identification of mutations to deliver the targeted inhibitor that counteract supposedly the effect of a mutated gene. Recent results have shown that this gene-centered strategy can be successful, but can also fall short in stopping progression. This is due to the many compensation mechanisms, cross-talks and feedback loops that enable the tumoral cell to escape treatment. Taking into account the regulatory network is necessary to establish which inhibitor or combination of inhibitors would achieve the best therapeutic results. Mathematical modelling of biological networks, together with high-quality pathway databases collecting our knowledge of the molecular circuitry of normal and tumoral cells, hold the hopes of an enhanced future for precision medicine in oncology.

OCSANA: optimal combinations of interventions from network analysis.

UNLABELLED: Targeted therapies interfering with specifically one protein activity are promising strategies in the treatment of diseases like cancer. However, accumulated empirical experience has shown that targeting multiple proteins in signaling networks involved in the disease is often necessary. Thus, one important problem in biomedical research is the design and prioritization of optimal combinations of interventions to repress a pathological behavior, while minimizing side-effects. OCSANA (optimal combinations of interventions from network analysis) is a new software designed to identify and prioritize optimal and minimal combinations of interventions to disrupt the paths between source nodes and target nodes. When specified by the user, OCSANA seeks to additionally minimize the side effects that a combination of interventions can cause on specified off-target nodes. With the crucial ability to cope with very large networks, OCSANA includes an exact solution and a novel selective enumeration approach for the combinatorial interventions' problem. AVAILABILITY: The latest version of OCSANA, implemented as a plugin for Cytoscape and distributed under LGPL license, is available together with source code at http://bioinfo.curie.fr/projects/ocsana.

BiNoM 2.0, a Cytoscape plugin for accessing and analyzing pathways using standard systems biology formats.

BACKGROUND: Public repositories of biological pathways and networks have greatly expanded in recent years. Such databases contain many pathways that facilitate the analysis of high-throughput experimental work and the formulation of new biological hypotheses to be tested, a fundamental principle of the systems biology approach. However, large-scale molecular maps are not always easy to mine and interpret. RESULTS: We have developed BiNoM (Biological Network Manager), a Cytoscape plugin, which provides functions for the import-export of some standard systems biology file formats (import from CellDesigner, BioPAX Level 3 and CSML; export to SBML, CellDesigner and BioPAX Level 3), and a set of algorithms to analyze and reduce the complexity of biological networks. BiNoM can be used to import and analyze files created with the CellDesigner software. BiNoM provides a set of functions allowing to import BioPAX files, but also to search and edit their content. As such, BiNoM is able to efficiently manage large BioPAX files such as whole pathway databases (e.g. Reactome). BiNoM also implements a collection of powerful graph-based functions and algorithms such as path analysis, decomposition by involvement of an entity or cyclic decomposition, subnetworks clustering and decomposition of a large network in modules. CONCLUSIONS: Here, we provide an in-depth overview of the BiNoM functions, and we also detail novel aspects such as the support of the BioPAX Level 3 format and the implementation of a new algorithm for the quantification of pathways for influence networks. At last, we illustrate some of the BiNoM functions on a detailed biological case study of a network representing the G1/S transition of the cell cycle, a crucial cellular process disturbed in most human tumors.

The genome of Tetranychus urticae reveals herbivorous pest adaptations.

The spider mite Tetranychus urticae is a cosmopolitan agricultural pest with an extensive host plant range and an extreme record of pesticide resistance. Here we present the completely sequenced and annotated spider mite genome, representing the first complete chelicerate genome. At 90 megabases T. urticae has the smallest sequenced arthropod genome. Compared with other arthropods, the spider mite genome shows unique changes in the hormonal environment and organization of the Hox complex, and also reveals evolutionary innovation of silk production. We find strong signatures of polyphagy and detoxification in gene families associated with feeding on different hosts and in new gene families acquired by lateral gene transfer. Deep transcriptome analysis of mites feeding on different plants shows how this pest responds to a changing host environment. The T. urticae genome thus offers new insights into arthropod evolution and plant-herbivore interactions, and provides unique opportunities for developing novel plant protection strategies.

Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data.

MOTIVATION: Cancer is a complex disease, triggered by mutations in multiple genes and pathways. There is a growing interest in the application of systems biology approaches to analyze various types of cancer-related data to understand the overwhelming complexity of changes induced by the disease. RESULTS: We reconstructed a regulatory module network using gene expression, microRNA expression and a clinical parameter, all measured in lymphoblastoid cell lines derived from patients having aggressive or non-aggressive forms of prostate cancer. Our analysis identified several modules enriched in cell cycle-related genes as well as novel functional categories that might be linked to prostate cancer. Almost one-third of the regulators predicted to control the expression levels of the modules are microRNAs. Several of them have already been characterized as causal in various diseases, including cancer. We also predicted novel microRNAs that have never been associated to this type of tumor. Furthermore, the condition-dependent expression of several modules could be linked to the value of a clinical parameter characterizing the aggressiveness of the prostate cancer. Taken together, our results help to shed light on the consequences of aggressive and non-aggressive forms of prostate cancer. AVAILABILITY: The complete regulatory network is available as an interactive supplementary web site at the following URL: http://bioinformatics.psb.ugent.be/webtools/pronet/.

Module network inference from a cancer gene expression data set identifies microRNA regulated modules.

BACKGROUND: MicroRNAs (miRNAs) are small RNAs that recognize and regulate mRNA target genes. Multiple lines of evidence indicate that they are key regulators of numerous critical functions in development and disease, including cancer. However, defining the place and function of miRNAs in complex regulatory networks is not straightforward. Systems approaches, like the inference of a module network from expression data, can help to achieve this goal. METHODOLOGY/PRINCIPAL FINDINGS: During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms. In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques. We show that several miRNAs are predicted as statistically significant regulators for various modules of tightly co-expressed genes. A detailed analysis of three of those modules demonstrates that the specific assignment of miRNAs is functionally coherent and supported by literature. We further designed a set of experiments to test the assignment of miR-200a as the top regulator of a small module of nine genes. The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells. CONCLUSIONS/SIGNIFICANCE: Our results show that a robust module network analysis of expression data can provide novel insights of miRNA function in important cellular processes. Such a computational approach, starting from expression data alone, can be helpful in the process of identifying the function of miRNAs by suggesting modules of co-expressed genes in which they play a regulatory role. As shown in this study, those modules can then be tested experimentally to further investigate and refine the function of the miRNA in the regulatory network.

Immune response to the 23-valent polysaccharide pneumococcal vaccine after the 7-valent conjugate vaccine in allogeneic stem cell transplant recipients: results from the EBMT IDWP01 trial.

The current recommendations for active immunization after stem cell transplant (SCT) include 3 doses of 7-valent pneumococcal conjugate vaccine (PCV7) from 3 months after transplant, followed by a 23-valent polysaccharide pneumococcal vaccine (PPV23). However, until now, the immune response to PPV23 after PCV7 has not been assessed after SCT. In the EBMT IDWP01 trial, 101 patients received 1 dose of PPV23 at 12 or 18 months, both after 3 doses of PCV7. The efficacy of PPV23 was assessed 1 month later and at 24 months after transplant by the pneumococcal serotype 1 and 5 antibody levels. Serotype 1 and 5 are not included in PCV7. Although the geometric mean concentrations were significantly higher 1 month after PPV23, for both antigens, the response rates (> or =0.15 microg/mL), in the range of 68-94%, were not different between groups independent of the assessment date. One PPV23 dose after 3 PCV7 doses, already known to increase the response to PCV7, also extends the serotype coverage given 12 or 18 months after transplant.

Impact of genetic polymorphisms on the risk of lipid disorders in patients on anti-HIV therapy.

Active anti-HIV therapy can induce hypertriglyceridemia, low high-density lipoprotein (HDL) and insulin resistance, eventually accompanied by clinical lipodystrophy, associated loss of subcutaneous adipose tissue and an increase in abdominal adiposity. The frequency of these metabolic disorders is approximately 50% and host genetic factors might confer particular susceptibility. Variants of apolipoproteins (apo) A5 and C3, interacting with APOE genotypes, have been associated with the severity of antiretroviral therapy-induced dyslipidemia and with occurrence of lipodystrophy, and for APOC3, with objective criteria of fat redistribution. Genetic polymorphisms of the nuclear transcription-factor sterol response element-binding proteins (SREBP1c) and of tumor necrosis factor-alpha (TNFalpha) have yielded contrasting results. Other candidate genes will be explored to define a pharmacogenomic strategy to identify patients at high risk of metabolic disorders upon antiretroviral therapy.

[Analysis of antiretroviral drugs-induced adverse effects]. / Analyse des effets indésirables des médicaments du VIH.

In 1999, The Regional Center of Pharmacogilance and the Department of Infectious Disease of the Toulouse University Hospital set up a system to improve the data collection about antiretroviral-induced adverse reactionss (ADRs). From November 1999 to April 2003, a resident of pharmacovigilance collected ADRs reported with antiretroviral drugs during 2 weekly medical consultations. A total of 613 ADRs corresponding to 428 patients were reported, classified as "non serious" in 88.6% of cases and required the withdrawal of suspected drugs in 57% of cases. Our data show an improvement of antiretroviral drug-induced ADRs reporting.