44 research outputs found
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
Construct validity of the psychological general well being index (PGWBI) in a sample of patients undergoing treatment for stress-related exhaustion: a rasch analysis
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
Analysis on the situation of subjective well-being and its influencing factors in patients with ankylosing spondylitis
BACKGROUND: To examine the subjective well-being (SWB) in patients with ankylosing spondylitis (AS) compared with the healthy controls, and to explore the associations between SWB and demographic characteristics, disease-specific variables in AS patients. METHODS: SWB was assessed with General Well-Being Schedule (GWBS) in 200 AS patients and 210 healthy controls. Comparisons among subgroups were performed to investigate how certain aspects operate as favorable or adverse factors in influencing SWB in the patients with AS. RESULTS: Both men and women with AS reported significantly impaired SWB on all scales of the GWBS except for the Control (O) scale. The results revealed that better sleep, lower disease activity and more family care predicted higher SWB. In AS patients, positive attitude towards therapy prospect was significantly associated with higher SWB. Therapy prospect refers to the hope of patients about the disease treatment. CONCLUSIONS: Compared with general population, SWB might be affected by the onset of AS. There are significant associations between SWB and sleep quality, BASDAI, APGAR, therapy prospect