Programming human pluripotent stem cells into white and brown adipocytes

The utility of human pluripotent stem cells is dependent on efficient differentiation protocols that convert these cells into relevant adult cell types. Here we report the robust and efficient differentiation of human pluripotent stem cells into white or brown adipocytes. We found that inducible expression of PPARG2 alone or combined with CEBPB and/or PRDM16 in mesenchymal progenitor cells derived from pluripotent stem cells programmed their development towards a white or brown adipocyte cell fate with efficiencies of 85%–90%. These adipocytes retained their identity independent of transgene expression, could be maintained in culture for several weeks, expressed mature markers and had mature functional properties such as lipid catabolism and insulin-responsiveness. When transplanted into mice, the programmed cells gave rise to ectopic fat pads with the morphological and functional characteristics of white or brown adipose tissue. These results indicate that the cells could be used to faithfully model human disease.Stem Cell and Regenerative Biolog

miMsg : a target enrichment algorithm for predicted miR-mRNA interactions based on relative ranking of matched expression data

Motivation: Algorithms predicting microRNA (miR)-mRNA interactions generate high numbers of possible interactions, many of which might be non-existent or irrelevant in a certain biological context. It is desirable to develop a transparent, user-friendly, unbiased tool to enrich miR-mRNA predictions. Results: The miMsg algorithm uses matched miR/mRNA expression data to enrich miR-mRNA predictions. It grades interactions by the number, magnitude and significance of misplacements in the combined ranking profiles of miR/mRNA expression assessed over multiple biological samples. miMsg requires minimal user input and makes no statistical assumptions. It identified 921 out of 56 262 interactions as top scoring and significant in an actual germ cell cancer dataset. Twenty-eight miR-mRNA pairs were deemed of highest interest based on ranking by miMsg and supported by current knowledge about validated interactions and biological function. To conclude, miMsg is an effective algorithm to reduce a high number of predicted interactions to a small set of high confidence interactions for further study

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

International audienceTwo acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. Findings: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94] vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). Interpretation: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. Funding: US National Institutes of Health and European Society of Intensive Care Medicine