Aagesenbradford0559

Inflammatory bowel disease (IBD) is a complex disease with variable presentation, progression, and response to therapies. Current disease classification is based on subjective clinical phenotypes. The peripheral blood immunophenome can reflect local inflammation, and thus we measured 39 circulating immune cell types in a large cohort of IBD and control subjects and performed immunotypephenotype associations.

We performed fluorescence-activated cell sorting or CyTOF analysis on blood from 728 Crohn's disease, 464 ulcerative colitis, and 334 non-IBD patients, with available demographics, endoscopic and clinical examinations and medication use.

We observed few immune cell types commonly affected in IBD (lowered natural killer cells, B cells, and CD45RA

CD8 T cells). Generally, the immunophenome was distinct between ulcerative colitis and Crohn's disease. Within disease subtype, there were further distinctions, with specific immune cell types associating with disease duration, behavior, and location. Thiopotentially explain the mechanism behind the superiority of combination therapy through the impact of thiopurines on pharmacokinetics of anti-TNFs.The cell membrane plays a central role in the fitness and performance of microbial cell factories and therefore it is an attractive engineering target. The goal of this work is to develop a systematic framework for identifying membrane features for use as engineering targets. The metrics that describe the composition of the membrane can be visualized as "knobs" that modulate various "outcomes", such as physical properties of the membrane and metabolic activity in the form of growth and productivity, with these relationships varying depending on the condition. We generated a set of strains with altered membrane lipid composition via expression of des, fabA and fabB and performed a rigorous characterization of these knobs and outcomes across several individual inhibitory conditions. Here, the knobs are the relative abundance of unsaturated lipids and lipids containing cyclic rings; the average lipid length, and the ratio of linear and non-linear lipids (L/nL ratio). The outcomes are membrane permeability, hydroysical properties can be used to predict the performance of biocatalysts in single and multiple inhibitory conditions, and possibly as an engineering target. In this manner, membrane properties can possibly be used as screening or selection metrics for library- or evolution-based strain engineering.Media and feed optimization have fueled many-fold improvements in mammalian biopharmaceutical production, but genome editing offers an emerging avenue for further enhancing cell metabolism and bioproduction. However, the complexity of metabolism, involving thousands of genes, makes it unclear which engineering strategies will result in desired traits. Here we present a comprehensive pooled CRISPR screen for CHO cell metabolism, including ~16,000 gRNAs against ~2500 metabolic enzymes and regulators. Using this screen, we identified a glutamine response network in CHO cells. Glutamine is particularly important since it is often over-fed to drive increased TCA cycle flux, but toxic ammonia may accumulate. With the screen we found one orphan glutamine-responsive gene with no clear connection to our network. Knockout of this novel and poorly characterized lipase, Abhd11, substantially increased growth in glutamine-free media by altering the regulation of the TCA cycle. Thus, the screen provides an invaluable targeted platform to comprehensively study genes involved in any metabolic trait, and elucidate novel regulators of metabolism.In cell culture processes cell growth and metabolism drive changes in the chemical environment of the culture. These environmental changes elicit reactor control actions, cell growth response, and are sensed by cell signaling pathways that influence metabolism. selleck kinase inhibitor The interplay of these forces shapes the culture dynamics through different stages of cell cultivation and the outcome greatly affects process productivity, product quality, and robustness. Developing a systems model that describes the interactions of those major players in the cell culture system can lead to better process understanding and enhance process robustness. Here we report the construction of a hybrid mechanistic-empirical bioprocess model which integrates a mechanistic metabolic model with subcomponent models for cell growth, signaling regulation, and the bioreactor environment for in silico exploration of process scenarios. Model parameters were optimized by fitting to a dataset of cell culture manufacturing process which exhibits variabilctor scaling.

We present the Clinical Trial Knowledge Base, a regularly updated knowledge base of discrete clinical trial eligibility criteria equipped with a web-based user interface for querying and aggregate analysis of common eligibility criteria.

We used a natural language processing (NLP) tool named Criteria2Query (Yuan et al., 2019) to transform free text clinical trial eligibility criteria from ClinicalTrials.gov into discrete criteria concepts and attributes encoded using the widely adopted Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and stored in a relational SQL database. A web application accessible via RESTful APIs was implemented to enable queries and visual aggregate analyses. We demonstrate CTKB's potential role in EHR phenotype knowledge engineering using ten validated phenotyping algorithms.

At the time of writing, CTKB contained 87,504 distinctive OMOP CDM standard concepts, including Condition (47.82%), Drug (23.01%), Procedure (13.73%), Measurement (24.70%) and Observation (5.28%), with 34.78% for inclusion criteria and 65.22% for exclusion criteria, extracted from 352,110 clinical trials. The average hit rate of criteria concepts in eMERGE phenotype algorithms is 77.56%.

CTKB is a novel comprehensive knowledge base of discrete eligibility criteria concepts with the potential to enable knowledge engineering for clinical trial cohort definition, clinical trial population representativeness assessment, electronical phenotyping, and data gap analyses for using electronic health records to support clinical trial recruitment.

CTKB is a novel comprehensive knowledge base of discrete eligibility criteria concepts with the potential to enable knowledge engineering for clinical trial cohort definition, clinical trial population representativeness assessment, electronical phenotyping, and data gap analyses for using electronic health records to support clinical trial recruitment.