Increased bactericidal activity of colistin on <i>Pseudomonas aeruginosa </i>biofilms in anaerobic conditions

Tolerance towards antibiotics of Pseudomonas aeruginosa biofilms is recognized as a major cause of therapeutic failure of chronic lung infection in cystic fibrosis (CF) patients. This lung infection is characterized by antibiotic-tolerant biofilms in mucus with zones of O(2) depletion mainly due to polymorphonuclear leukocytic activity. In contrast to the main types of bactericidal antibiotics, it has not been possible to establish an association between the bactericidal effects of colistin and the production of detectable levels of OH ˙ on several strains of planktonic P. aeruginosa. Therefore, we propose that production of OH ˙ may not contribute significantly to the bactericidal activity of colistin on P. aeruginosa biofilm. Thus, we investigated the effect of colistin treatment on biofilm of wild-type PAO1, a catalase-deficient mutant (ΔkatA) and a colistin-resistant CF isolate cultured in microtiter plates in normoxic- or anoxic atmosphere with 1 mM nitrate. The killing of bacteria during colistin treatment was measured by CFU counts, and the OH⋅ formation was measured by 3(′)-(p-hydroxylphenyl fluorescein) fluorescein (HPF) fluorescence. Validation of the assay was done by hydrogen peroxide treatment. OH⋅ formation was undetectable in aerobic PAO1 biofilms during 3 h of colistin treatment. Interestingly, we demonstrate increased susceptibility of P. aeruginosa biofilms towards colistin during anaerobic conditions. In fact, the maximum enhancement of killing by anaerobic conditions exceeded 2 logs using 4 mg L(−1) of colistin compared to killing at aerobic conditions

Comparative analysis of 12 different kits for bisulfite conversion of circulating cell-free DNA

<p>Blood circulating cell-free DNA (cfDNA) is becoming popular in the search of promising predictive and prognostic biomarkers. Among these biomarkers, cfDNA methylation markers have especially gained considerable attention. A significant challenge in the utilization of cfDNA methylation markers is the limited amount of cfDNA available for analyses; reportedly, bisulfite conversion (BSC) reduce cfDNA amounts even further. Nevertheless, few efforts have focused on ensuring high cfDNA conversion efficiency and recovery after BSC. To compare cfDNA recovery of different BSC methods, we compared 12 different commercially available BSC kits. We tested whether DNA recovery was affected by the molecular weight and/or quantity of input DNA. We also tested BSC efficiency for each kit. We found that recovery varied for DNA fragments of different lengths: certain kits recovered short fragments better than others, and only 3 kits recovered DNA fragments of <100 bp well. In contrast, DNA input amount did not seem to affect DNA recovery: for quantities spanning between 820 and ∼25,000 genome equivalents per BSC, a linear relation was found between input and recovery amount. Overall, mean recovery ranged between 9 and 32%, with BSC efficiency of 97–99.9%. When plasma cfDNA was used as input for BSC, recovery varied from 22% for the poorest and 66% for the best performing kits, while conversion efficiency ranged from 96 to 100% among different kits. In conclusion, clear performance differences exist between commercially available BSC kits, both in terms of DNA recovery and conversion efficiency. The choice of BSC kit can substantially impact the amount of converted cfDNA available for downstream analysis, which is critical in a cfDNA methylation marker setting.</p

Detection and characterization of lung cancer using cell-free DNA fragmentomes

Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer

Genome-wide cell-free DNA fragmentation in patients with cancer

Cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer1. However, characteristics of the origins and molecular features of cell-free DNA are poorly understood. Here we developed an approach to evaluate fragmentation patterns of cell-free DNA across the genome, and found that profiles of healthy individuals reflected nucleosomal patterns of white blood cells, whereas patients with cancer had altered fragmentation profiles. We used this method to analyse the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric or bile duct cancer and 245 healthy individuals. A machine learning model that incorporated genome-wide fragmentation features had sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity, with an overall area under the curve value of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer. The results of these analyses highlight important properties of cell-free DNA and provide a proof-of-principle approach for the screening, early detection and monitoring of human cancer

Genome-wide cell-free DNA fragmentation in patients with cancer

Cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer1. However, characteristics of the origins and molecular features of cell-free DNA are poorly understood. Here we developed an approach to evaluate fragmentation patterns of cell-free DNA across the genome, and found that profiles of healthy individuals reflected nucleosomal patterns of white blood cells, whereas patients with cancer had altered fragmentation profiles. We used this method to analyse the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric or bile duct cancer and 245 healthy individuals. A machine learning model that incorporated genome-wide fragmentation features had sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity, with an overall area under the curve value of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer. The results of these analyses highlight important properties of cell-free DNA and provide a proof-of-principle approach for the screening, early detection and monitoring of human cancer