Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell

Ist die Hamofiltration nach akutem nierenversagen bei Herz-Gefass-chirurgischen Patienten im hohen Alter gerechtfertigt? [Is hemofiltration following acute kidney failure in elderly cardiovascular surgery patients justified?]

In a retrospective study, 47 patients with acute renal failure following cardiovascular surgery were evaluated in the Cardiovascular Surgery ICU of the University Hospital, Zurich, from 1991 to 1994. The object of the study was to investigate the contribution of hemofiltration for acute renal failure following cardiovascular surgery. The aim was further to identify risk factors which impair survival. We found that overall mortality was 70.2%, but was influenced by the patients' age. In patients under 60 (n = 9) the mortality rate was 55.6%; in patients from 60 to 69 (n = 19) the mortality rate was 57.9%. However, in patients aged 70 and over (n = 19), there was an increase in mortality to 89.5%. Specifically, the outcome was tested in a stepwise logistic regression and we found that recovery of renal function following hemofiltration was the most important factor favoring survival (p < 0.001). Further statistical analysis revealed that age 70 years or over was one of the main risk factors leading to death (p < 0.03), whereas preoperative renal insufficiency had no influence on postoperative outcome (p < 0.29). We conclude that postoperative renal function represents the crucial factor in survival. Although hemofiltration achieved adequate results in all groups, the patients of 70 years and more experienced a significantly higher mortality rate. This suggests that the increased mortality of the elderly was not due to renal failure in itself but rather was related to polymorbidity. Hence we propose that in the latter group of elderly patients candidates for hemofiltration are to be very carefully selected, particularly if they have additional complications