Project description:ALL is the most common form of childhood cancer with >80% cured with contemporary treatment protocols. Accurate risk stratification in childhood ALL is essential to avoid under- and over-treatment. Currently, we use presenting clinical, biological features, and minimal residual disease (MRD) quantitation to risk stratify patients. Although whole genome gene expression profiling (GEP) can accurately classify patients with ALL into various WHO 2008 defined subgroups, its value in predicting relapse remained to be defined. We hypothesized that global time-series GEPs of bone marrow (BM) samples at diagnosis and specific points during initial remission-induction therapy can measure the success of cytoreduction and be used for relapse prediction. We generated time-series GEPs from 210 children with de novo ALL at diagnosis, and Day 8 of remission-induction therapy. We computed the time-series changes from diagnosis to follow-up time point of remission-induction, termed Effective Response Metric (ERM), that measures both the magnitude and direction of time-series change in multi-dimensional gene space towards the normal centroid, and we compared its ability to predict relapse against contemporary risk assignment methods including NCI criteria, cytogenetics and MRD.
Project description:Bacteriophage – host dynamics and interactions are important for microbial community composition and ecosystem function. Nonetheless, empirical evidence in engineered environment is scarce. Here, we examined phage and prokaryotic community composition of four anaerobic digestors in full-scale wastewater treatment plants (WWTPs) across China. Despite relatively stable process performance in biogas production, both phage and prokaryotic groups fluctuated monthly over a year of study period. Nonetheless, there were significant correlations in their α- and β-diversities between phage and prokaryotes. Phages explained 40.6% of total prokaryotic community composition, much higher than the explainable power by abiotic factors (14.5%). Consequently, phages were significantly (P<0.010) linked to parameters related to process performance including biogas production and volatile solid concentrations. Association network analyses showed that phage-prokaryote pairs were deeply rooted, and two network modules were exclusively comprised of phages, suggesting a possibility of co-infection. Those results collectively demonstrate phages as a major biotic factor in controlling bacterial composition. Therefore, phages may play a larger role in shaping prokaryotic dynamics and process performance of WWTPs than currently appreciated, enabling reliable prediction of microbial communities across time and space.
Project description:We aimed to investigate the microbial community composition in patients with intracerebral hemorrhage (ICH) and its effect on prognosis. The relationship between changes in bacterial flora and the prognosis of spontaneous cerebral hemorrhage was studied in two cohort studies. Fecal samples from healthy volunteers and patients with intracerebral hemorrhage were subjected to 16S rRNA sequencing at three time points: T1 (within 24 hours of admission), T2 (3 days post-surgery), and T3 (7 days post-surgery) using Illumina high-throughput sequencing technology.
