Nouvelle méthode statistique pour l'analyse de données de ChIP-chip

International audienceLa méthode de Chromatin ImmunoPrecipitation on chip (ChIP on chip ou ChIP-chip) a pour but de détecter les sites de fixation des protéines (généralement des facteurs de transcription) sur la molécule d'ADN. L'analyse statistique des données consiste a rechercher des régions de pics significatifs synonymes de sites de fixation. La méthode que nous avons élaborée est issue de la théorie des valeurs extrêmes et particulièrement de la méthode POT (Peaks Over Threshold). Cette méthode consiste à modéliser les données de queues de distribution, en ne retenant que les valeurs dépassant un certain seuil, elle a la particularité de modéliser d'une part les intensités de dépassement de seuil, mais aussi les positions d'occurrences de ces dépassements de seuil. Cette méthode va nous permettre de déterminer un seuil au delà duquel les pics pourront être considérés comme significatifs

Role of gut microbiota and bacterial translocation in acute intestinal injury and mortality in patients admitted in ICU for septic shock

IntroductionSepsis is a life-threatening organ dysfunction with high mortality rate. The gut origin hypothesis of multiple organ dysfunction syndrome relates to loss of gut barrier function and the ensuing bacterial translocation. The aim of this study was to describe the evolution of gut microbiota in a cohort of septic shock patients over seven days and the potential link between gut microbiota and bacterial translocation.MethodsSixty consecutive adult patients hospitalized for septic shock in intensive care units (ICU) were prospectively enrolled. Non-inclusion criteria included patients with recent or scheduled digestive surgery, having taken laxatives, pre- or probiotic in the previous seven days, a progressive digestive neoplasia, digestive lymphoma, chronic inflammatory bowel disease, moribund patient, and pregnant and lactating patients. The primary objective was to evaluate the evolution of bacterial diversity and richness of gut microbiota during seven days in septic shock. Epidemiological, clinical and biological data were gathered over seven days. Gut microbiota was analyzed through a metagenomic approach. 100 healthy controls were selected among healthy blood donors for reference basal 16S rDNA values.ResultsSignificantly lower bacterial diversity and richness was observed in gut microbiota of patients at Day 7 compared with Day 0 (p<0.01). SOFA score at Day 0, Acute Gastrointestinal Injury (AGI) local grade, septic shock origin and bacterial translocation had an impact on alpha diversity. A large increase in Enterococcus genus was observed at Day 7 with a decrease in Enterobacterales, Clostridiales, Bifidobacterium and other butyrate-producing bacteria.DiscussionThis study shows the importance of bacterial translocation during AGI in septic shock patients. This bacterial translocation decreases during hospitalization in ICUs in parallel to the decrease of microbiota diversity. This work highlights the role of gut microbiota and bacterial translocation during septic shock

Development of statistical methodologies applied to genomics data : microarray, ChIP-chip and ChIP-Seq.

La recherche dans le domaine de la génomique génère de données colossales dont la dimension ne cesse de s'accroître avec la technologie. Pour traiter cette masse d'information, la statistique est devenue un outil indispensable. Ce nouveau type de données représente un véritable challenge dans la mesure où ces données sont de très grande dimension, qu'elles sont très "bruitées" et qu'il n'existe généralement pas de "golden standard" permettant de valider les résultats. Au cours de cette thèse, nous nous sommes intéressés à l'analyse statistique de trois types de données : les puces à ADN, les ChIP-chip et les ChIP-Seq. Pour chacune d'entres elles, une nouvelle approche a été mise au point. Dans le cas des données de puces à ADN, la méthode GAGG permet de détecter les gènes différentiellement exprimés et de les grouper par type de profils. Pour ce faire, un Algorithme Génétique est utilisé de manière à optimiser deux critères liés à des méthodes voisines de l'ACP et des k-means. Pour les données de ChIP-chip, la méthode POTChIPS a été réalisée. Elle permet de repérer sur le génome, les sites de fixation d'une protéine d'intérêt (ex : un facteur de transcription). Dans cette méthode, une extraction des pics du signal est réalisée puis un seuil de significativité est déterminé à partir d'une modélisation POT. Enfin, pour ce qui est des données de ChIP-Seq, l'objectif est le même que pour les ChIP-chip, à savoir, repérer les sites de fixation d'une protéine sur l'ADN. La méthode POTSeq, mise au point au cours de cette thèse, est une adaptation de POTChIPS aux données de ChIP-Seq.Research in Genomics produces very huge data which still increase with technology. Statistics is becoming essential to treat this amount of information. These new kind of data represent a great challenge in data analysis because of the great dimensions, the important background and the absence of "golden standard" which could allow to validate the obtained results. During this PhD thesis, we focused on statistical analysis for three kinds of data: DNA microarray, ChIP-chip and ChIP-Seq. For each one, a new approach have been proposed. For DNA microarrays, the GAGG method allows to detect differentially expressed genes and to cluster them by profile types. To do so, a Genetic Algorithm is used in order to optimize two criteria related to two nearby methods of PCA and k-means. In the case of ChIP-chip data, the POTChIPS method have been proposed. It allows to detect the binding sites of a protein of interest (a transcription factor e.g.) along the genome. In this method a peak extraction i realized then a significant threshold is obtained from a POT modelization. Finally, for ChIP-Seq data, the goal is the same that the one of chIP-chip, i.e., to find on DNA the binding sites of a protein of interest. The POTSeq method is an adaptation of POTChIPS for ChIP-Seq.La méthode POTSeq, mise au point au cours de cette thèse, est une adaptation de POTChIPS aux données de ChIP-Seq

Mise au point de méthodologies statistiques appliquées à des données issues de la génomique (puces à ADN, ChIP-chip et ChIP-Seq.)

La recherche dans le domaine de la génomique génère de données colossales dont la dimension ne cesse de s'accroître avec la technologie. Pour traiter cette masse d'information, la statistique est devenue un outil indispensable. Ce nouveau type de données représente un véritable challenge dans la mesure où ces données sont de très grande dimension, qu'elles sont très "bruitées" et qu'il n'existe généralement pas de "golden standard" permettant de valider les résultats. Au cours de cette thèse, nous nous sommes intéressés à l'analyse statistique de trois types de données : les puces à ADN, les ChIP-chip et les ChIP-Seq. Pour chacune d'entres elles, une nouvelle approche a été mise au point. Dans le cas des données de puces à ADN, la méthode GAGG permet de détecter les gènes différentiellement exprimés et de les grouper par type de profils. Pour ce faire, un Algorithme Génétique est utilisé de manière à optimiser deux critères liés à des méthodes voisines de l'ACP et des k-means. Pour les données de ChIP-chip, la méthode POTChIPS a été réalisée. Elle permet de repérer sur le génome, les sites de fixation d'une protéine d'intérêt (ex : un facteur de transcription). Dans cette méthode, une extraction des pics du signal est réalisée puis un seuil de significativité est déterminé à partir d'une modélisation POT. Enfin, pour ce qui est des données de ChIP-Seq, l'objectif est le même que pour les ChIP-chip, à savoir, repérer les sites de fixation d'une protéine sur l'ADN. La méthode POTSeq, mise au point au cours de cette thèse, est une adaptation de POTChIPS aux données de ChIP-Seq.Research in Genomics produces very huge data which still increase with technology. Statistics is becoming essential to treat this amount of information. These new kind of data represent a great challenge in data analysis because of the great dimensions, the important background and the absence of "golden standard" which could allow to validate the obtained results. During this PhD thesis, we focused on statistical analysis for three kinds of data: DNA microarray, ChIP-chip and ChIP-Seq. For each one, a new approach have been proposed. For DNA microarrays, the GAGG method allows to detect differentially expressed genes and to cluster them by profile types. To do so, a Genetic Algorithm is used in order to optimize two criteria related to two nearby methods of PCA and $k$-means. In the case of ChIP-chip data, the POTChIPS method have been proposed. It allows to detect the binding sites of a protein of interest (a transcription factor e.g.) along the genome. In this method a peak extraction i realized then a significant threshold is obtained from a POT modelization. Finally, for ChIP-Seq data, the goal is the same that the one of chIP-chip, i.e., to find on DNA the binding sites of a protein of interest. The POTSeq method is an adaptation of POTChIPS for ChIP-Seq.La méthode POTSeq, mise au point au cours de cette thèse, est une adaptation de POTChIPS aux données de ChIP-Seq.MONTPELLIER-BU Sciences (341722106) / SudocSudocFranceF

A new approach to assessing calcium status via a machine learning algorithm.

peer reviewed("[en] BACKGROUND AND AIMS: Calcium plays a fundamental role in biological processes. Ionized calcium (Ca2+), is the biologically active fraction, but in practice total or corrected calcium assays are routinely used to determine calcium status. MATERIALS AND METHODS: We retrospectively compared total and corrected calcium to assess the calcium status, with ionized calcium which is considered for now like the best indicator. To compensate for their lack of performance we created a machine learning algorithm to predict calcium status. RESULTS: Corrected calcium performed less well than total calcium with 58% and 74% agreement, respectively, in our population. Total calcium was especially good for hypocalcemic samples: 93% agreement versus 45% for normocalcemic and 54% for hypercalcemic samples. Corrected calcium was especially good for hypercalcemic and normocalcemic samples: 90% and 84% agreement respectively versus 40% for hypocalcemic samples. Corrected calcium is mainly faulty in hypoalbuminemia, acid-base disorders, renal insufficiency, hyperphosphatemia, or inflammatory syndrome. With our ML algorithm, we obtained 81% correct classifications. Its main advantage is that its performance are not influenced by the variables studied or the calcium status. CONCLUSION: In many situations, corrected calcium should not be used. Our ML algorithm may make a better assessment of calcium status than total calcium. Finally, if doubt, an ionized calcium assay should be performed.","[en] ",""

Antiseptic Agents for Chronic Wounds: A Systematic Review

International audienceIn many parts of the world, antiseptic agents remain non-indicated in chronic wound care. In the current context of bacterial resistance to antibiotics and the development of new-generation antiseptic agents, wound antisepsis represents an asset for the prevention of wound infection. We aimed to evaluate four common antiseptic agents in chronic wound care complete healing. The review protocol was based on the Cochrane Handbook for Systematic Reviews of Intervention and devised in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement guidelines. Five databases and three clinical trials registries were searched from inception to 30 June 2021 without language restrictions. We included randomised trials evaluating the efficacy of antiseptic agents in chronic wound care in adults. Interventions considered were those using antiseptics for cleansing or within a dressing. Risk of bias was assessed using the bias excel tool provided by the Bristol Academy. Evidence quality was assessed using Grading of Recommendation Assessment, Development and Evaluation (GRADE) criteria. Of 838 studies, 6 were finally included, with a total of 725 patients. The included studies assessed iodine (cadexomer or povidone iodine) (n = 3), polyhexanide (n = 2), and octenidine (n = 1). Limited evidence suggested a better wound healing completion with iodine compared to saline (two randomised controlled trials (RCT), 195 patients, pooled RR 1.85 (95%CI (1.27 to 2.69)), moderate-quality evidence). There was not enough evidence to suggest a difference in wound healing using octenidine or polyhexamide. None of the antiseptic agents influenced adverse event occurrence compared to saline

Dynamics of community-acquired meningitis syndrome outbreaks in southern France

International audienceIn southern France, cases of community-acquired meningitis syndrome (CAM) are typically clustered as outbreaks with determinants which remain unknown. This 61-month retrospective investigation in Nîmes and Marseille university hospital laboratories, yielded 2,209/20,779 (10.63%) documented CAM cases caused by 62 different micro-organisms, represented by seasonal viral etiologies (78.8%), including Enterovirus, Herpes Simplex Virus (HSV), and Varicella-Zoster Virus (VZV; 1,620/2,209 = 73.4%). Multi correspondence analysis revealed an association of infection with age and sex, with the risk of infection being relatively higher in young men, as confirmed by Fisher’s exact test ( p < 10 −3 ). Bacterial meningitis accounted for 20% of cases, mostly caused by Streptococcus pneumoniae (27.4% of cases), Neisseria meningitidis (12.5%), and Haemophilus influenzae (9.5%) with bacteria/virus coinfection (0.9%), and only six cases of documented fungal meningitis. In total, 62.6% of cases, of which 88.7% were undocumented, arose from 10 outbreaks. 33.2% of undocumented cases were aged >60 years compared to 19.2% of documented cases ( p < 0.001), and viral infection was more common in the summer (87.5%) compared to other seasons (72.3%; p < 0.001). Outbreaks most often started in Nîmes and moved eastward toward Marseille at a speed of ~9 km/day, and these dynamics significantly correlated with atmospheric temperature, especially during summer outbreaks. In particular, the incidence of Enterovirus-driven outbreaks correlated with temperature, revealing correlation coefficients of 0.64 in Nîmes and 0.72 in Marseille, and its occurrence in Marseille lagged that in Nîmes by 1–2 weeks. Tracing the dynamics of CAM outbreak during this retrospective investigation in southern France yielded a speed of displacement that correlated with the variation in temperature between both cities, and these results provide clues for the next occurrence of undocumented outbreaks

Optimal combination of early biomarkers for infection and sepsis diagnosis in the emergency department: The BIPS study

International audienceObjective: : To define the best combination of biomarkers for the diagnosis of infection and sepsis in the emergency room. Methods: : In this prospective study, consecutive patients with a suspicion of infection in the emergency room were included. Eighteen different biomarkers measured in plasma, and twelve biomarkers measured on monocytes, neutrophils, B and T-lymphocytes were studied and the best combinations determined by a gradient tree boosting approach. Results: : Overall, 291 patients were included and analysed, 148 with bacterial infection, and 47 with viral infection. The best biomarker combination which first allowed the diagnosis of bacterial infection, included HLA-DR (human leukocyte antigen DR) on monocytes, MerTk (Myeloid-epithelial-reproductive tyrosine kinase) on neutrophils and plasma metaloproteinase-8 (MMP8) with an area under the curve (AUC) = 0.94 [95% confidence interval (IC95): 0.91;0.97]. Among patients in whom a bacterial infection was excluded, the combination of CD64 expression, and CD24 on neutrophils and CX3CR1 on monocytes ended to an AUC = 0.98 [0.96;1] to define those with a viral infection. Conclusion: : In a convenient cohort of patients admitted with a suspicion of infection, two different combinations of plasma and cell surface biomarkers were performant to identify bacterial and viral infection

Clinical Features and Outcomes of Enterococcal Bone and Joint Infections and Factors Associated with Treatment Failure over a 13-Year Period in a French Teaching Hospital

International audienceEnterococcal bone and joint infections (BJIs) are reported to have poor outcomes, but there are conflicting results. This study aimed to describe the clinical characteristics and outcomes of patients with enterococcal BJI and to assess the factors associated with treatment failure. We conducted a retrospective cohort study at Nimes University Hospital from January 2007 to December 2020. The factors associated with treatment failure were assessed using a Cox model. We included 90 consecutive adult patients, 11 with native BJIs, 40 with prosthetic joint infections and 39 with orthopedic implant-associated infections. Two-thirds of patients had local signs of infection, but few (9%) had fever. Most BJIs were caused by Enterococcus faecalis (n = 82, 91%) and were polymicrobial (n = 75, 83%). The treatment failure rate was 39%, and treatment failure was associated with coinfection with Staphylococcus epidermidis (adjusted hazard ratio = 3.04, confidence interval at 95% [1.31–7.07], p = 0.01) and with the presence of local signs of inflammation at the time of diagnosis (aHR = 2.39, CI 95% [1.22–4.69], p = 0.01). Our results confirm the poor prognosis of enterococcal BJIs, prompting clinicians to carefully monitor for local signs of infection and to optimize the medical-surgical management in case of coinfections, especially with S. epidermidis