Prosthetic Joint Infections due to Candida Species: A Multicenter International Study.

BACKGROUND: Prosthetic joint infection (PJI) caused by Candida spp is a severe complication of arthroplasty. We investigated the outcomes of Candida PJI. METHODS: This was a retrospective observational multinational study including patients diagnosed with Candida-related PJI between 2010 and 2021. Treatment outcome was assessed at 2-year follow-up. RESULTS: A total of 269 patients were analyzed. Median age was 73.0 (interquartile range [IQR], 64.0-79.0) years; 46.5% of patients were male and 10.8% were immunosuppressed. Main infection sites were hip (53.0%) and knee (43.1%), and 33.8% patients had fistulas. Surgical procedures included debridement, antibiotics, and implant retention (DAIR) (35.7%), 1-stage exchange (28.3%), and 2-stage exchange (29.0%). Candida spp identified were Candida albicans (55.8%), Candida parapsilosis (29.4%), Candida glabrata (7.8%), and Candida tropicalis (5.6%). Coinfection with bacteria was found in 51.3% of cases. The primary antifungal agents prescribed were azoles (75.8%) and echinocandins (30.9%), administered for a median of 92.0 (IQR, 54.5-181.3) days. Cure was observed in 156 of 269 (58.0%) cases. Treatment failure was associated with age >70 years (OR, 1.811 [95% confidence interval {CI}: 1.079-3.072]), and the use of DAIR (OR, 1.946 [95% CI: 1.157-3.285]). Candida parapsilosis infection was associated with better outcome (OR, 0.546 [95% CI: .305-.958]). Cure rates were significantly different between DAIR versus 1-stage exchange (46.9% vs 67.1%, P = .008) and DAIR versus 2-stage exchange (46.9% vs 69.2%, P = .003), but there was no difference comparing 1- to 2-stage exchanges (P = .777). CONCLUSIONS: Candida PJI prognosis seems poor, with high rate of failure, which does not appear to be linked to immunosuppression, use of azoles, or treatment duration.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The Ectocarpus genome and the independent evolution of multicellularity in brown algae.

Brown algae (Phaeophyceae) are complex photosynthetic organisms with a very different evolutionary history to green plants, to which they are only distantly related. These seaweeds are the dominant species in rocky coastal ecosystems and they exhibit many interesting adaptations to these, often harsh, environments. Brown algae are also one of only a small number of eukaryotic lineages that have evolved complex multicellularity (Fig. 1). We report the 214 million base pair (Mbp) genome sequence of the filamentous seaweed Ectocarpus siliculosus (Dillwyn) Lyngbye, a model organism for brown algae, closely related to the kelps (Fig. 1). Genome features such as the presence of an extended set of light-harvesting and pigment biosynthesis genes and new metabolic processes such as halide metabolism help explain the ability of this organism to cope with the highly variable tidal environment. The evolution of multicellularity in this lineage is correlated with the presence of a rich array of signal transduction genes. Of particular interest is the presence of a family of receptor kinases, as the independent evolution of related molecules has been linked with the emergence of multicellularity in both the animal and green plant lineages. The Ectocarpus genome sequence represents an important step towards developing this organism as a model species, providing the possibility to combine genomic and genetic approaches to explore these and other aspects of brown algal biology further.

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.

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.

Microbiological Profile of Instrumented Spinal Infections: 10-Year Study at a French Spine Center.

OBJECTIVE: The objective was to compare the microbiological characteristics and treatment of early and late surgical site infections (SSIs) in instrumented spinal surgery. METHODS: Those patients admitted for SSIs in a single center between January 2010 and December 2022 were included. The subjects were divided into early (eSSIs) and late (lSSIs) SSIs, and demographic, microbiological, treatment, and follow-up data were collected. RESULTS: Instrumented spinal surgery was performed in 2136 patients. Ninety-six cases of infections were identified (prevalence = 4.5%), with 47.9% eSSIs and 52.1% lSSIs. In 58.7% of the cases, the eSSIs were monomicrobial: Staphylococcus aureus (37%) and Enterobacterales (33.3%) were the main bacteria involved. In 66% of the cases, the lSSIs, were monomicrobial: Cutibacterium acnes (30.3%) and staphylococci were predominant. Enterobacterales were isolated in more than 70% of the polymicrobial samples in both the eSSIs and lSSIs. The treatment of the eSSIs mostly consisted of lavage-debridement surgery associated with antibiotic treatment, while the treatment of the lSSIs combined hardware removal or replacement and long-duration antibiotic treatment. A negative outcome was observed in 17.1% of the eSSIs and 5.7% of the lSSIs. Enterobacterales were associated with negative outcomes of eSSIs. CONCLUSIONS: Enterobacterales were found in most of the polymicrobial infections regardless of the time of infection onset. Further large studies should be conducted to precisely determine the management and prevention regarding the increasing Gram-negative bacteria SSIs.

Early loss of bone mineral density is correlated with a gain of fat mass in patients starting a protease inhibitor containing regimen: the prospective Lipotrip study.

BACKGROUND: HIV-infected patients starting antiretroviral treatment (ART) experience deep and early disorders in fat and bone metabolism, leading to concomitant changes in fat mass and bone mineral density. METHODS: We conducted a prospective study in treatment-naive HIV-infected patients randomized to receive two nucleoside reverse transcriptase inhibitors in combination with either a protease inhibitor (PI) or a non-nucleosidic reverse transcriptase inhibitor (NNRTI), to evaluate early changes in body composition, bone mineral density and metabolic markers as differentially induced by antiretroviral therapies. We measured changes in markers of carbohydrate, of fat and bone metabolism, and, using dual-emission X-ray absorptiometry (DXA), body composition and bone mineral density (BMD). Complete data on changes between baseline and after 21 months treatment were available for 35 patients (16 in the PI group and 19 in the NNRTI group). RESULTS: A significant gain in BMI and in total and lower limb fat mass was recorded only in patients receiving PI. A loss of lumbar BMD was observed in both groups, being higher with PI. Plasma markers of bone metabolism (alkaline phosphatase, osteocalcin, collagen crosslaps) and levels of parathormone and of 1,25diOH-vitamin D3 significantly increased in both groups, concomitant with a decline in 25OH-vitamin D3. Lipids and glucose levels increased in both groups but rise in triglyceride was more pronounced with PI. A correlation between loss of BMD and gain of fat mass is observed in patients starting PI. CONCLUSIONS: We evidenced an early effect of ART on lipid and bone metabolisms. PI lead to a significant gain in fat mass correlated with a sharp drop in BMD but active bone remodelling is evident with all antiretroviral treatments, associated with low vitamin D levels and hyperparathyroidism. In parallel, signs of metabolic restoration are evident. However, early increases in lean and fat mass, triglycerides, waist circumference and leptin are much more pronounced with PI.

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.

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.

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.

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.

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.