Improvement of the recombinant phytase expression by intermittent feeding of glucose during the induction phase of methylotrophic yeast Pichia pastoris.

PURPOSE: For growth of methylotrophic yeast, glycerol is usually used as a carbon source. Glucose is used in some cases, but not widely consumed due to strong repressive effect on AOX1 promoter. However, glucose is still considered as a carbon source of choice since it has low production cost and guarantees growth rate comparable to glycerol. RESULTS: In flask cultivation of the recombinant yeast, Pichia pastoris GS115(pPIC9K-appA38M), while methanol induction point(OD600) and methanol concentration significantly affected the phytase expression, glucose addition in induction phase could enhance phytase expression. The optimal flask cultivation conditions illustrated by Response Surface Methodology were 10.37 OD600 induction point, 2.02 h before methanol feeding, 1.16% methanol concentration and 40.36µL glucose feeding amount(for 20 mL culture volume) in which the expressed phytase activity was 613.4 ± 10.2U/mL, the highest activity in flask cultivation. In bioreactor fermentation, the intermittent glucose feeding showed several advantageous results such as 68 h longer activity increment, 149.2% higher cell density and 200.1% higher activity compared to the sole methanol feeding method. These results implied that remaining glucose at induction point might exhibit a positive effect on the phytase expression. CONCLUSION: Glucose intermittent feeding could be exploited for economic phytase production and the other recombinant protein expression by P. pastoris GS115.

Relationship Between Feeding to Sleep During Infancy and Subsequent Childhood Disease Burden.

OBJECTIVE: To investigate the association of feeding to sleep during infancy and subsequent childhood health burdens. STUDY DESIGN: Information was collected from the parents of children who participated in the national health screening survey when the child was 9-12 months old. The exposure group included participants who were fed to sleep. The primary outcome was all-cause hospital admission (inpatient care, intensive care unit [ICU] admission, or general anesthesia) after age 24 months. Secondary outcomes were subsequent childhood diseases (ie, adenoidectomy and/or tonsillectomy, nasal polyps, allergic rhinitis, acute otitis media, asthma, pneumonia, and aspiration pneumonia), and growth status, as measured by weight-to-age and height-to-age z-scores. RESULTS: The study cohort consisted of 224 075 children who participated in the health screening program, 29 392 of whom (13.1%; 51% males) were fed to sleep. Exposure was associated with an increased risk of all-cause hospitalization after age 24 months (hazard ratio [HR], 1.05; 95% CI, 1.03-1.07), but not with admission to an ICU or receipt of general anesthesia. This also was related to adenoidectomy and/or tonsillectomy (HR, 1.08; 95% CI, 1.01-1.15), dental caries (HR, 1.32; 95% CI, 1.23-1.40), asthma (HR, 1.14; 95% CI, 1.14-1.24), pneumonia (HR, 1.10; 95% CI, 1.07-1.13), overweight (HR, 1.06; 95% CI, 1.03-1.09), and obesity (HR, 1.11; 95% CI, 1.06-1.16). CONCLUSIONS: Several adverse health outcomes are related to feeding to sleep during early childhood.

Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction.

In order to improve our understanding of cancer and develop multi-layered theoretical models for the underlying mechanism, it is essential to have enhanced understanding of the interactions between multiple levels of genomic data that contribute to tumor formation and progression. Although there exist recent approaches such as a graph-based framework that integrates multi-omics data including copy number alteration, methylation, gene expression, and miRNA data for cancer clinical outcome prediction, most of previous methods treat each genomic data as independent and the possible interplay between them is not explicitly incorporated to the model. However, cancer is dysregulated by multiple levels in the biological system through genomic, epigenomic, transcriptomic, and proteomic level. Thus, genomic features are likely to interact with other genomic features in the different genomic levels. In order to deepen our knowledge, it would be desirable to incorporate such inter-relationship information when integrating multi-omics data for cancer clinical outcome prediction. In this study, we propose a new graph-based framework that integrates not only multi-omics data but inter-relationship between them for better elucidating cancer clinical outcomes. In order to highlight the validity of the proposed framework, serous cystadenocarcinoma data from TCGA was adopted as a pilot task. The proposed model incorporating inter-relationship between different genomic features showed significantly improved performance compared to the model that does not consider inter-relationship when integrating multi-omics data. For the pair between miRNA and gene expression data, the model integrating miRNA, for example, gene expression, and inter-relationship between them with an AUC of 0.8476 (REI) outperformed the model combining miRNA and gene expression data with an AUC of 0.8404. Similar results were also obtained for other pairs between different levels of genomic data. Integration of different levels of data and inter-relationship between them can aid in extracting new biological knowledge by drawing an integrative conclusion from many pieces of information collected from diverse types of genomic data, eventually leading to more effective screening strategies and alternative therapies that may improve outcomes.