CHOmics: A web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines.

Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an interactive visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and interactively. The tool was also designed with the flexibility to accommodate other types of omics data and thereby enabling multi-omics comparison and visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, visualization and management with expectations to promote the broader use of omics in CHO cell research.

Acute kidney injury predicts outcomes of non-critically ill patients.

OBJECTIVE: To evaluate whether acute kidney injury (AKI), defined as an increase in the serum creatinine level of 0.3 mg/dL or more within 48 hours, predicts outcomes of non-critically ill patients. PATIENTS AND METHODS: Among the adults admitted from June 1, 2005, to June 30, 2007, to the medical wards of a community teaching hospital, 735 patients with AKI and 5089 controls were identified. Demographic and health information, serum creatinine values, and outcomes were abstracted from patients' computerized medical records. Outcomes of patients with AKI were compared with those of controls. In an additional case-control analysis, more detailed clinical information was abstracted from the medical records of 282 pairs of randomly selected, age-matched AKI cases and controls. Conditional multivariate logistic regression analyses were used to adjust for potential confounders of AKI effect on outcomes. RESULTS: Overall, patients with AKI had higher in-hospital mortality (14.8% vs 1.5%; P<.001), longer lengths of stay (median 7.9 vs 3.7 days; P<.001), and higher rates of transfer to critical care areas (28.6% vs 4.3%; P<.001); survivors were more likely to be discharged to an extended care facility (43.1% vs 20.3%; P<.001). Conditional multivariate logistic regression analyses of the 282 pairs of cases and controls showed that patients with AKI were 8 times more likely to die in hospital (odds ratio [OR], 7.9; 95% CI [confidence interval], 2.9-15.3) and were 5 times more likely to have prolonged (>or=7 days) hospital stays (OR, 5.2; 95% CI, 3.5-7.9) and require intensive care (OR, 4.7; 95% CI, 2.7-8.1), after adjustment for age, comorbidities, and other potential confounders. CONCLUSION: In this study, AKI was associated with adverse outcomes in non-critically ill patients.