Characterizing Dynamic Changes in the Human Blood Transcriptional Network

Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectivity, are strongly associated with metabolic diseases. In this study, we explored which genes drive the changes of gene expression patterns in response to time and food intake. We applied the Granger causality test and the dynamic Bayesian network to gene expression data generated from blood samples collected at multiple time points during the course of a day. The simulation result shows that combining many short time series together is as powerful to infer Granger causality as using a single long time series. Using the Granger causality test, we identified genes that were supported as the most likely causal candidates for the coordinated temporal changes in the network. These results show that PER1 is a key regulator of the blood transcriptional network, in which multiple biological processes are under circadian rhythm regulation. The fasted and fed dynamic Bayesian networks showed that over 72% of dynamic connections are self links. Finally, we show that different processes such as inflammation and lipid metabolism, which are disconnected in the static network, become dynamically linked in response to food intake, which would suggest that increasing nutritional load leads to coordinate regulation of these biological processes. In conclusion, our results suggest that food intake has a profound impact on the dynamic co-regulation of multiple biological processes, such as metabolism, immune response, apoptosis and circadian rhythm. The results could have broader implications for the design of studies of disease association and drug response in clinical trials

Germline variants in IL4, MGMT and AKT1 are associated with prostate cancer-specific mortality: An analysis of 12,082 prostate cancer cases

BackgroundProstate cancer (PCa) is a leading cause of mortality and genetic factors can influence tumour aggressiveness. Several germline variants have been associated with PCa-specific mortality (PCSM), but further replication evidence is needed.MethodsTwenty-two previously identified PCSM-associated genetic variants were genotyped in seven PCa cohorts (12,082 patients; 1544 PCa deaths). For each cohort, Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals for risk of PCSM associated with each variant. Data were then combined using a meta-analysis approach.ResultsFifteen SNPs were associated with PCSM in at least one of the seven cohorts. In the meta-analysis, after adjustment for clinicopathological factors, variants in the MGMT (rs2308327; HR 0.90; p-value = 3.5 × 10−2) and IL4 (rs2070874; HR 1.22; p-value = 1.1 × 10−3) genes were confirmed to be associated with risk of PCSM. In analyses limited to men diagnosed with local or regional stage disease, a variant in AKT1, rs2494750, was also confirmed to be associated with PCSM risk (HR 0.81; p-value = 3.6 × 10−2).ConclusionsThis meta-analysis confirms the association of three genetic variants with risk of PCSM, providing further evidence that genetic background plays a role in PCa-specific survival. While these variants alone are not sufficient as prognostic biomarkers, these results may provide insights into the biological pathways modulating tumour aggressiveness.</div

Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation

DNA variation can be used as a systematic source of perturbation in segregating populations as a way to infer regulatory networks via the integration of large-scale, high-dimensional molecular profiling data

Nitrogen Transformations in Wetland Soil Cores Measured by (sup15)N Isotope Pairing and Dilution at Four Infiltration Rates

The effect of water infiltration rate (IR) on nitrogen cycling in a saturated wetland soil was investigated by applying a (sup15)N isotope dilution and pairing method. Water containing [(sup15)N]nitrate was infiltrated through 10-cm-long cores of sieved and homogenized soil at rates of 72, 168, 267, and 638 mm day(sup-1). Then the frequencies of (sup30)N(inf2), (sup29)N(inf2), (sup15)NO(inf3)(sup-), and (sup15)NH(inf4)(sup+) in the outflow water were measured. This method allowed simultaneous determination of nitrification, coupled and uncoupled denitrification, and nitrate assimilation rates. From 3% (at the highest IR) to 95% (at the lowest IR) of nitrate was removed from the water, mainly by denitrification. The nitrate removal was compensated for by the net release of ammonium and dissolved organic nitrogen. Lower oxygen concentrations in the soil at lower IRs led to a sharper decrease in the nitrification rate than in the ammonification rate, and, consequently, more ammonium leaked from the soil. The decreasing organic-carbon-to-nitrogen ratio (from 12.8 to 5.1) and the increasing light A(inf250)/A(inf365) ratio (from 4.5 to 5.2) indicated an increasing bioavailability of the outflowing dissolved organic matter with increasing IR. The efflux of nitrous oxide was also very sensitive to IR and increased severalfold when a zone of low oxygen concentration was close to the outlet of the soil cores. N(inf2)O then constituted 8% of the total gaseous N lost from the soil.</jats:p