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Distribution of microbial communities and core microbiome in successive wound grades of diabetic foot ulcer individuals.
糖尿病足溃疡个体连续伤口分级中微生物群落和核心微生物组的分布。
- 影响因子:4.18
- DOI:10.1128/AEM.02608-19
- 作者列表:"Jnana A","Muthuraman V","Varghese VK","Chakrabarty S","Murali TS","Ramachandra L","Shenoy KR","Rodrigues GS","Prasad SS","Dendukuri D","Morschhauser A","Nestler J","Peter H","Bier FF","Satyamoorthy K
- 发表时间:2020-01-10
Abstract
:Diabetic foot ulcer (DFU) is a major complication of diabetes with high morbidity and mortality rates. Pathogenesis of DFUs is governed by a complex milieu of environmental and host factors. The empirical treatment is initially based on wound severity since culturing and profiling the antibiotic sensitivity of wound-associated microbes is time consuming. Hence, a thorough and rapid analysis of the microbial landscape is a major requirement towards devising evidence-based interventions. Towards this, 122 wound (100 diabetic and 22 non-diabetic) samples were sampled for their bacterial community structure using both culture-based and next-generation 16S rRNA based metagenomics approach. Both the approaches showed that the Gram-negative microbes were more abundant in the wound microbiome. The core microbiome consisted of bacterial genera including Alcaligenes, Pseudomonas, Burkholderia, and Corynebacterium in decreasing order of average relative abundance. Despite the heterogenous nature and extensive sharing of microbes, an inherent community structure was apparent as revealed by a cluster analysis based on Euclidean distances. Facultative anaerobes (26.5%) were predominant in Wagner grade 5 while strict anaerobes were abundant in Wagner grade 1 (26%). A non-metric dimensional scaling analysis could not clearly discriminate samples based on HbA1c levels. Sequencing approach revealed the presence of major culturable species even in samples with no bacterial growth in culture-based approach. Our study indicates that a) composition of core microbial community varies with wound severity, b) polymicrobial species distribution is individual-specific, and c) antibiotic susceptibility varies with individuals. Our study suggests the need to evolve better-personalized care for better wound management therapies.IMPORTANCE: Chronic non-healing diabetic foot ulcers (DFU) is a serious complication of diabetes and is further exacerbated by bacterial colonization. Microbial burden in the wound of each individual displays diverse morphological and physiological characteristics with unique patterns of host-pathogen interactions, antibiotic resistance and virulence. Treatment involves empirical decisions until definitive results on the causative wound pathogens and their antibiotic susceptibility profiles are available. Hence, there is a need for rapid and accurate detection of these polymicrobial communities for effective wound management. Deciphering microbial communities will aid clinicians to tailor their treatment specifically to the microbes prevalent in the DFU at the time of assessment. This may reduce DFU associated morbidity and mortality while impeding the rise of multi drug resistant microbes.
摘要
糖尿病足溃疡 (DFU) 是糖尿病的主要并发症,具有较高的发病率和死亡率。DFUs 的发病机制受环境和宿主因素的复杂环境支配。经验性治疗最初是基于伤口严重程度,因为培养和分析伤口相关微生物的抗生素敏感性是耗时的。因此,对微生物景观进行彻底和快速的分析是制定循证干预措施的主要要求。为此,使用基于培养的和基于下一代 16 S rRNA 的宏基因组学方法,对 122 个伤口 (100 个糖尿病患者和 22 个非糖尿病患者) 样本进行细菌群落结构取样。这两种方法都表明革兰氏阴性微生物在伤口微生物群中更丰富。核心微生物组由产碱杆菌、假单胞菌、伯克霍尔德菌和棒状杆菌等细菌属组成,平均相对丰度递减。尽管微生物具有异质性和广泛的共享,但基于欧氏距离的聚类分析揭示了一个固有的群落结构。Wagner 5 级以兼性厌氧菌为主 (26.5%),Wagner 1 级以严格厌氧菌为主 (26%)。非度量尺度分析不能基于 HbA1c 水平明确区分样本。测序方法揭示了即使在基于培养的方法中没有细菌生长的样本中也存在主要的可培养物种。我们的研究表明,a) 核心微生物群落的组成随伤口严重程度而变化,b) 多微生物种类分布为个体特异性,c) 抗生素敏感性随个体而变化。我们的研究表明,需要发展更好的个性化护理,以获得更好的伤口管理疗法。重要性: 慢性不愈合糖尿病足溃疡 (DFU) 是糖尿病的严重并发症,并因细菌定植而进一步加剧。每个个体伤口中的微生物负荷表现出不同的形态和生理特征,具有宿主-病原体相互作用、抗生素耐药性和毒力的独特模式。治疗涉及经验决策,直到对致病伤口病原体及其抗生素敏感性谱有明确结果。因此,需要快速准确地检测这些多微生物群落,以进行有效的伤口管理。破译微生物群落将有助于临床医生专门针对评估时 DFU 中普遍存在的微生物进行治疗。这可能会降低 DFU 相关的发病率和死亡率,同时阻碍多重耐药微生物的上升。
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METHODS:Aims We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. Methods A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). Results A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69–0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64–0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA_1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72–0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66–0.78), compared to an AUC of 0.67 (95% CI 0.63–0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62–0.70) using the Teede model. Conclusions A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
METHODS:Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy. Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring^ 1 – 4 . GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes^ 5 , 6 . Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.
METHODS::Repurposing of currently approved medications is an attractive option for the development of novel treatment strategies against physiological and infectious diseases. The antidiabetic sulfonylurea glyburide has demonstrated off-target capacity to inhibit activation of the NLRP3 inflammasome in a variety of disease models, including vaginal candidiasis, caused primarily by the fungal pathogen Candida albicans Therefore, we sought to determine which of the currently approved sulfonylurea drugs prevent the release of interleukin 1β (IL-1β), a major inflammasome effector, during C. albicans challenge of the human macrophage-like THP1 cell line. Findings revealed that the second-generation antidiabetics (glyburide, glisoxepide, gliquidone, and glimepiride), which exhibit greater antidiabetic efficacy than prior iterations, demonstrated anti-inflammatory effects with various degrees of potency as determined by calculation of 50% inhibitory concentrations (IC50s). These same compounds were also effective in reducing IL-1β release during noninfectious inflammasome activation (e.g., induced by lipopolysaccharide [LPS] plus ATP), suggesting that their anti-inflammatory activity is not specific to C. albicans challenge. Moreover, treatment with sulfonylurea drugs did not impact C. albicans growth and filamentation or THP1 viability. Finally, the use of ECE1 and Candidalysin deletion mutants, along with isogenic NLRP3-/- cells, demonstrated that both Candidalysin and NLRP3 are required for IL-1β secretion, further confirming that sulfonylureas suppress inflammasome signaling. Moreover, challenge of THP1 cells with synthetic Candidalysin peptide demonstrated that this toxin is sufficient to activate the inflammasome. Treatment with the experimental inflammasome inhibitor MCC950 led to similar blockade of IL-1β release, suggesting that Candidalysin-mediated inflammasome activation can be inhibited independently of potassium efflux. Together, these results demonstrate that the second-generation antidiabetic sulfonylureas retain anti-inflammatory activity and may be considered for repurposing against immunopathological diseases, including vaginal candidiasis.