Whole-Genome Bisulfite Sequencing Protocol for the Analysis of Genome-Wide DNA Methylation and Hydroxymethylation Patterns at Single-Nucleotide Resolution.

The analysis of genome-wide epigenomic alterations including DNA methylation and hydroxymethylation has become a subject of intensive research for many biological and clinical questions. DNA methylation analysis bears the particular promise to supplement or replace biochemical and imaging-based tests for the next generation of personalized medicine. Whole-genome bisulfite sequencing (WGBS) using next-generation sequencing technologies is currently considered the gold standard for a comprehensive and quantitative analysis of DNA methylation throughout the genome. However, bisulfite conversion does not allow distinguishing between cytosine methylation and hydroxymethylation requiring an additional chemical or enzymatic step to identify hydroxymethylated cytosines. Here, we provide a detailed protocol based on a commercial kit for the preparation of sequencing libraries for the comprehensive whole-genome analysis of DNA methylation and/or hydroxymethylation. The protocol is based on the construction of sequencing libraries from limited amounts of input DNA by ligation of methylated adaptors to the fragmented DNA prior to bisulfite conversion. For analyses requiring a quantitative distinction between 5-methylcytosine and 5-hydroxymethylcytosines levels, an oxidation step is included in the same workflow to perform oxidative bisulfite sequencing (OxBs-Seq). In this case, two sequencing libraries will be generated and sequenced: a classic methylome following bisulfite conversion and analyzing modified cytosines (not distinguishing between methylated and hydroxymethylated cytosines) and a methylome analyzing only methylated cytosines, respectively. Hydroxymethylation levels are deduced from the differences between the two reactions. We also provide a step-by-step description of the data analysis using publicly available bioinformatic tools. The described protocol has been successfully applied to different human and plant samples and yields robust and reproducible results.

Single cell transcriptome sequencing of stimulated and frozen human peripheral blood mononuclear cells.

Peripheral blood mononuclear cells (PBMCs) are blood cells that are a critical part of the immune system used to fight off infection, defending our bodies from harmful pathogens. In biomedical research, PBMCs are commonly used to study global immune response to disease outbreak and progression, pathogen infections, for vaccine development and a multitude of other clinical applications. Over the past few years, the revolution in single-cell RNA sequencing (scRNA-seq) has enabled an unbiased quantification of gene expression in thousands of individual cells, which provides a more efficient tool to decipher the immune system in human diseases. In this work, we generate scRNA-seq data from human PBMCs at high sequencing depth (>100,000 reads/cell) for more than 30,000 cells, in resting, stimulated, fresh and frozen conditions. The data generated can be used for benchmarking batch correction and data integration methods, and to study the effect of freezing-thawing cycles on the quality of immune cell populations and their transcriptomic profiles.

Infective endocarditis after transcatheter pulmonary valve implantation in patients with congenital heart disease: Distinctive features.

The introduction of transcatheter pulmonary valve implantation (TPVI) has greatly benefited the management of right ventricular outflow tract dysfunction. Infective endocarditis (IE) is a feared complication of TPVI that affects valve durability and patient outcomes. Current recommendations provide only limited guidance on the management of IE after TPVI (TPVI-IE). This article, by a group of experts in congenital heart disease in children and adults, interventional cardiology, infectious diseases including IE, and microbiology, provides a comprehensive review of the current evidence on TPVI-IE, including its incidence, risk factors, causative organisms, diagnosis, and treatment. The incidence of TPVI-IE varies from 13-91/1000 person-years for Melody valves to 8-17/1000 person-years for SAPIEN valves. Risk factors include history of IE, DiGeorge syndrome, immunosuppression, male sex, high residual transpulmonary gradient and portal of bacteria entry. Staphylococci and streptococci are the most common culprits, whereas Staphylococcus aureus is associated with the most severe disease. In addition to the modified Duke criteria, a high residual gradient warrants a strong suspicion. Imaging studies are helpful for the diagnosis. Intravenous antibiotics guided by blood culture results are the mainstay of treatment. Invasive re-intervention may be required. TPVI-IE in patients with congenital heart disease exhibits several distinctive features. Whether specific valve types are associated with a higher risk of TPVI-IE requires further investigation. Patient and parent education regarding IE prevention may have a role to play and should be offered to all patients.

Chlamydia psittaci endocarditis: A case report and literature review.

OBJECTIVES: Blood-culture-negative infective endocarditis (BCNE) is found in 2 to 48% of cases of infective endocarditis (IE) (Houpikian and Raoult, 2005) [1].IE and vertebral osteomyelitis due to Chlamydia sp. are difficult to diagnose. PATIENTS AND METHODS: A case report of Chlamydia psittaci IE is provided, associated with a literature review. RESULTS: We report the first case of Chlamydia psittaci IE, revealed by a spondylodiscitis. Questioning of the patient, imaging, serologies and PCR techniques on valves confirmed the diagnosis. CONCLUSION: C. psittaci IE is rare but probably underdiagnosed. In case of negative blood cultures, questioning patients with IE about their contacts with animals is relevant. PCR techniques are reference tools for diagnosis confirmation when valve or vertebral samples are available.

Reactive astrocytes promote proteostasis in Huntington's disease through the JAK2-STAT3 pathway.

Huntington's disease is a fatal neurodegenerative disease characterized by striatal neurodegeneration, aggregation of mutant Huntingtin and the presence of reactive astrocytes. Astrocytes are important partners for neurons and engage in a specific reactive response in Huntington's disease that involves morphological, molecular and functional changes. How reactive astrocytes contribute to Huntington's disease is still an open question, especially because their reactive state is poorly reproduced in experimental mouse models. Here, we show that the JAK2-STAT3 pathway, a central cascade controlling astrocyte reactive response, is activated in the putamen of Huntington's disease patients. Selective activation of this cascade in astrocytes through viral gene transfer reduces the number and size of mutant Huntingtin aggregates in neurons and improves neuronal defects in two complementary mouse models of Huntington's disease. It also reduces striatal atrophy and increases glutamate levels, two central clinical outcomes measured by non-invasive magnetic resonance imaging. Moreover, astrocyte-specific transcriptomic analysis shows that activation of the JAK2-STAT3 pathway in astrocytes coordinates a transcriptional program that increases their intrinsic proteolytic capacity, through the lysosomal and ubiquitin-proteasome degradation systems. This pathway also enhances their production and exosomal release of the co-chaperone DNAJB1, which contributes to mutant Huntingtin clearance in neurons. Together, our results show that the JAK2-STAT3 pathway controls a beneficial proteostasis response in reactive astrocytes in Huntington's disease, which involves bi-directional signalling with neurons to reduce mutant Huntingtin aggregation, eventually improving disease outcomes.

Methylated ccfDNA from plasma biomarkers of Alzheimer's disease using targeted bisulfite sequencing.

Aim: Noninvasive biomarkers such as methylated ccfDNA from plasma could help to support the diagnosis of Alzheimer's disease (AD). Methods: A targeted sequencing protocol was developed to identify candidate biomarkers of AD in methylated ccfDNA extracted from plasma. Results: The authors identified differentially methylated CpGs, regions of which were the same as those identified in previous AD studies. Specifically, a differentially methylated CpG of the LHX2 gene previously identified in a plasma study of AD was replicated in the study. The MBP and DUSP22 regions have been identified in other brain studies of AD and in the authors' study. Conclusion: Although these biomarkers must be validated in other cohorts, methylated ccfDNA could be a relevant noninvasive biomarker in AD.

Currently, the diagnosis of Alzheimer's disease (AD) is based on symptoms and medical imaging, and definitive clinical diagnosis is only possible postmortem. The identification of noninvasive biomarkers such as methylated ccfDNA is crucial for the diagnosis, prognosis and monitoring of AD. However, the analysis of ccfDNA from plasma is a challenge because it is highly fragmented and present in low amounts and originates from various tissues. The authors developed a targeted sequencing protocol using genes previously reported in AD literature (brain, blood and plasma) to identify potential noninvasive biomarkers in plasma. The authors identified positions identical to those in the literature as well as potential novel sites located in the promoter, exon and intron regions of these genes. Although these results must be validated in a large cohort, methylated ccfDNA could be a useful noninvasive biomarker for AD.

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.

DEVEA: an interactive shiny application for Differential Expression analysis, data Visualization and Enrichment Analysis of transcriptomics data.

We are at a time of considerable growth in the use and development of transcriptomics studies and subsequent in silico analysis. RNA sequencing is one of the most widely used approaches, now integrated in many studies.  The processing of these data may typically require a noteworthy number of steps, statistical knowledge, and coding skills which is not accessible to all scientists. Despite the undeniable development of software applications over the years to address this concern, it is still possible to improve.  Here we present DEVEA, an R shiny application tool developed to perform differential expression analysis, data visualization and enrichment pathway analysis mainly from transcriptomics data, but also from simpler gene lists with or without statistical values.  Its intuitive and easy-to-manipulate interface facilitates gene expression exploration through numerous interactive figures and tables, statistical comparisons of expression profile levels between groups and further meta-analysis such as enrichment analysis, without bioinformatics expertise. DEVEA performs a thorough analysis from multiple and flexible input data representing distinct analysis stages. From them, it produces dynamic graphs and tables, to explore the expression levels and statistical differential expression analysis results. Moreover, it generates a comprehensive pathway analysis to extend biological insights. Finally, a complete and customizable HTML report can be extracted for further result exploration outside the application. DEVEA is accessible at https://shiny.imib.es/devea/ and the source code is available on our GitHub repository https://github.com/MiriamRiquelmeP/DEVEA.

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.

Low Density In-Medium Effects on Light Clusters from Heavy-Ion Data.

The modification of the ground state properties of light atomic nuclei in the nuclear and stellar medium is addressed, using chemical equilibrium constants evaluated from a new analysis of the intermediate energy heavy-ion (Xe+Sn) collision data measured by the INDRA Collaboration. Three different reactions are considered, mainly differing by the isotopic content of the emission source. The thermodynamic conditions of the data samples are extracted from the measured multiplicities allowing for a parametrization of the in-medium modification, determined with the single hypothesis that the different nuclear species in a given sample correspond to a unique common value for the density of the expanding source. We show that this correction, which was not considered in previous analyses of chemical constants from heavy-ion collisions, is necessary, since the observables of the analyzed systems show strong deviations from the expected results for an ideal gas of free clusters. This dataset is further compared to a relativistic mean-field model, and seen to be reasonably compatible with a universal correction of the attractive σ-meson coupling.

RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis.

Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.

The use of labelled leucocyte scintigraphy to evaluate chronic periprosthetic joint infections: a retrospective multicentre study on 168 patients.

Labelled leucocyte scintigraphy (LS) is regarded as helpful when exploring bone and joint infections. The aim of this study was to evaluate the utility of LS for the diagnosis of chronic periprosthetic joint infections (PJIs) in patients exhibiting arthroplastic loosening. One hundred sixty-eight patients were referred to centres for treatment of complex PJI. One hundred fifty underwent LS using 99mTc-HMPAO (LLS); 18 also underwent anti-granulocyte scintigraphy (AGS) and 13 additional SPECT with tomodensitometry imaging (SPECT-CT). The LS results were compared with bone scan data. For all, the final diagnoses were determined microbiologically; perioperative samples were cultured. LS values were examined, as well as sensitivity by microorganism, anatomical sites, and injected activity. LS results were also evaluated according to the current use of antibiotics or not. The sensitivity, specificity, and positive predictive value of LLS were 72%, 60%, and 80%, respectively. LLS performed better than did AGS. SPECT-CT revealed the accurate locations of infections. The sensitivity of LS was not significantly affected by the causative pathogen or the injected activity. No correlation was evident between the current antibiotic treatment and the LS value. The test was more sensitive for knee (84%) than hip arthroplasty (57%) but was less specific for knee (52% vs. 75%). Sensitivity and specificity of LLS varied by the location of infection bone scan provide no additional value in PJI diagnosis. Current antibiotic treatment seems to have no influence on LS sensitivity as well as labelling leukocyte activity or pathogens responsible for chronic PJI.

Systematic analysis of TruSeq, SMARTer and SMARTer Ultra-Low RNA-seq kits for standard, low and ultra-low quantity samples.

High-throughput RNA-sequencing has become the gold standard method for whole-transcriptome gene expression analysis, and is widely used in numerous applications to study cell and tissue transcriptomes. It is also being increasingly used in a number of clinical applications, including expression profiling for diagnostics and alternative transcript detection. However, despite its many advantages, RNA sequencing can be challenging in some situations, for instance in cases of low input amounts or degraded RNA samples. Several protocols have been proposed to overcome these challenges, and many are available as commercial kits. In this study, we systematically test three recent commercial technologies for RNA-seq library preparation (TruSeq, SMARTer and SMARTer Ultra-Low) on human biological reference materials, using standard (1 mg), low (100 ng and 10 ng) and ultra-low (<1 ng) input amounts, and for mRNA and total RNA, stranded and unstranded. The results are analyzed using read quality and alignment metrics, gene detection and differential gene expression metrics. Overall, we show that the TruSeq kit performs well with an input amount of 100 ng, while the SMARTer kit shows decreased performance for inputs of 100 and 10 ng, and the SMARTer Ultra-Low kit performs relatively well for input amounts <1 ng. All the results are discussed in detail, and we provide guidelines for biologists for the selection of an RNA-seq library preparation kit.

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.

Learning Differential Module Networks Across Multiple Experimental Conditions.

Module network inference is a statistical method to reconstruct gene regulatory networks, which uses probabilistic graphical models to learn modules of coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here, we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.

Correction to: Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE).

[This corrects the article DOI: 10.4056/sigs.5279417.].

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.