Metabolic systems analysis of LPS induced endothelial dysfunction applied to sepsis patient stratification.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked FilesEndothelial dysfunction contributes to sepsis outcome. Metabolic phenotypes associated with endothelial dysfunction are not well characterised in part due to difficulties in assessing endothelial metabolism in situ. Here, we describe the construction of iEC2812, a genome scale metabolic reconstruction of endothelial cells and its application to describe metabolic changes that occur following endothelial dysfunction. Metabolic gene expression analysis of three endothelial subtypes using iEC2812 suggested their similar metabolism in culture. To mimic endothelial dysfunction, an in vitro sepsis endothelial cell culture model was established and the metabotypes associated with increased endothelial permeability and glycocalyx loss after inflammatory stimuli were quantitatively defined through metabolomics. These data and transcriptomic data were then used to parametrize iEC2812 and investigate the metabotypes of endothelial dysfunction. Glycan production and increased fatty acid metabolism accompany increased glycocalyx shedding and endothelial permeability after inflammatory stimulation. iEC2812 was then used to analyse sepsis patient plasma metabolome profiles and predict changes to endothelial derived biomarkers. These analyses revealed increased changes in glycan metabolism in sepsis non-survivors corresponding to metabolism of endothelial dysfunction in culture. The results show concordance between endothelial health and sepsis survival in particular between endothelial cell metabolism and the plasma metabolome in patients with sepsis.RANNIS Landspitali Reykjavik Rigshospitalet Copenhage

Conditions Associated with the Cystic Fibrosis Defect Promote Chronic Pseudomonas aeruginosa Infection.

Rationale: Progress has been made in understanding how the cystic fibrosis (CF) basic defect produces lung infection susceptibility. However, it remains unclear why CF exclusively leads to chronic infections that are non-invasive and highly resistant to eradication. While biofilm formation has been suggested as a mechanism, recent work raises questions about the role of biofilms in CF. Objectives: To learn how airway conditions attributed to CFTR dysfunction could lead to chronic infection, and to determine if biofilm-inhibiting genetic adaptations that are common in CF isolates affect the capacity of P. aeruginosa to develop chronic infection phenotypes. Methods: We studied P. aeruginosa isolates grown in agar and mucus gels containing sputum from CF patients and measured their susceptibility to killing by antibiotics and host defenses. We also measured the invasive virulence of P. aeruginosa grown in sputum gels using airway epithelial cells and a murine infection model. Measurements and Main Results: We found that conditions likely to result from increased mucus density, hyper-inflammation, and defective bacterial killing could all cause P. aeruginosa to grow in bacterial aggregates. Aggregated growth markedly increased the resistance of bacteria to killing by host defenses and antibiotics, and reduced their invasiveness. In addition, we found that biofilm-inhibiting mutations do not impede aggregate formation in gel growth environments. Conclusions: Our findings suggest that conditions associated with several CF pathogenesis hypotheses could cause the non-invasive and resistant infection phenotype, independently of the bacterial functions needed for biofilm formation

Intraoperative DNA methylation classification of brain tumors impacts neurosurgical strategy

Background Brain tumor surgery must balance the benefit of maximal resection against the risk of inflicting severe damage. The impact of increased resection is diagnosis-specific. However, the precise diagnosis is typically uncertain at surgery due to limitations of imaging and intraoperative histomorphological methods. Novel and accurate strategies for brain tumor classification are necessary to support personalized intraoperative neurosurgical treatment decisions. Here, we describe a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and machine learning algorithms. Methods We evaluated 6 independent cohorts containing 105 patients, including 50 pediatric and 55 adult patients. Ultra-low coverage whole-genome sequencing was performed on nanopore flow cells. Data were analyzed using copy number variation and ad hoc random forest classifier for the genome-wide methylation-based classification of the tumor. Results Concordant classification was obtained between nanopore DNA methylation analysis and a full neuropathological evaluation in 93 of 105 (89%) cases. The analysis demonstrated correct diagnosis in 6/6 cases where frozen section evaluation was inconclusive. Results could be returned to the operating room at a median of 97 min (range 91-161 min). Precise classification of the tumor entity and subtype would have supported modification of the surgical strategy in 12 out of 20 patients evaluated intraoperatively. Conclusion Intraoperative nanopore sequencing combined with machine learning diagnostics was robust, sensitive, and rapid. This strategy allowed DNA methylation-based classification of the tumor to be returned to the surgeon within a timeframe that supports intraoperative decision making

MR elastography identifies regions of extracellular matrix reorganization associated with shorter survival in glioblastoma patients

Abstract Background Biomechanical tissue properties of glioblastoma tumors are heterogeneous, but the molecular mechanisms involved and the biological implications are poorly understood. Here, we combine magnetic resonance elastography (MRE) measurement of tissue stiffness with RNA sequencing of tissue biopsies to explore the molecular characteristics of the stiffness signal. Methods MRE was performed preoperatively in 13 patients with glioblastoma. Navigated biopsies were harvested during surgery and classified as “stiff” or “soft” according to MRE stiffness measurements (|G*|norm). Twenty-two biopsies from eight patients were analyzed by RNA sequencing. Results The mean whole-tumor stiffness was lower than normal-appearing white matter. The surgeon’s stiffness evaluation did not correlate with the MRE measurements, which suggests that these measures assess different physiological properties. Pathway analysis of the differentially expressed genes between “stiff” and “soft” biopsies showed that genes involved in extracellular matrix reorganization and cellular adhesion were overexpressed in “stiff” biopsies. Supervised dimensionality reduction identified a gene expression signal separating “stiff” and “soft” biopsies. Using the NIH Genomic Data Portal, 265 glioblastoma patients were divided into those with (n = 63) and without (n = 202) this gene expression signal. The median survival time of patients with tumors expressing the gene signal associated with “stiff” biopsies was 100 days shorter than that of patients not expressing it (360 versus 460 days, hazard ratio: 1.45, P < .05). Conclusion MRE imaging of glioblastoma can provide noninvasive information on intratumoral heterogeneity. Regions of increased stiffness were associated with extracellular matrix reorganization. An expression signal associated with “stiff” biopsies correlated with shorter survival of glioblastoma patients