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

Validation of two generic patient-reported outcome measures in patients with type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>Prior to using a generic patient-reported outcome measure (PRO), the measure should be validated within the target population. The purpose of the current study was to validate two generic measures in patients with type 2 diabetes.</p> <p>Methods</p> <p>Patients with type 2 diabetes in Scotland and England completed two generic measures: EQ-5D and Psychological General Well-Being Index (PGWB). Two diabetes-specific measures were administered: ADS and DSC-R. Analyses assessed reliability and validity.</p> <p>Results</p> <p>There were 130 participants (53 Scotland; 77 England; 64% male; mean age = 55.7 years). Responses on the EQ-5D and PGWB reflected moderate impairment consistent with previous diabetes samples: mean EQ-5D Index score, 0.75; EQ-5D VAS, 68.8; PGWB global score, 67.9. All scales of the PGWB demonstrated good internal consistency reliability (Cronbach's alpha = 0.77 to 0.97). The EQ-5D and PGWB demonstrated convergent validity through significant correlations with the ADS (r = 0.48 to 0.61), DSC-R scales (r = 0.33 to 0.81 except ophthalmology subscale), and Body Mass Index (r = 0.15 to 0.38). The EQ-5D and PGWB discriminated between groups of patients known to differ in diabetes-related characteristics (e.g., history of hypoglycemia).</p> <p>Conclusion</p> <p>Results support the use of the EQ-5D and PGWB among patients with type 2 diabetes, possibly in combination with condition-specific measures.</p

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

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

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