The Role of Macrophage Substrate Metabolism on Obesity-Induced Inflammation

Obesity has become increasingly prevalent worldwide. Studies characterize obesity as a low-grade chronic state of inflammation and identify macrophages as having a role in this response. Macrophage polarization may influence the pro- and anti-inflammatory responses associated with obese and lean phenotypes, respectively. This study exploited the differences in substrate utilization between M1 and M2 polarized macrophages to observe whether there was a change in the inflammatory phenotype with obesity. M1-polarized macrophages are pro-inflammatory and use glucose as fuel whereas M2-polarized macrophages are anti-inflammatory and preferentially use fatty acids as fuel. Specifically in this experiment, mice containing macrophages that were FATP1 knockout or wildtype were fed either a low fat diet (10% kcal from fat, LFD)or a high fat diet (45% kcal from fat, HFD) to test our hypothesis that FATP1 is important in macrophage M2 polarization. Four groups were used in this experiment: low fat-fedwildtype and knockout (LFD FATP1B+/+ and LFD FATP1B-/-), and high fat-fedwildtype and knockout (HFD FATP1B+/+ and HFD FATP1B-/-). HFD FATP1B-/- mice gained more weight and had heavier epididymal white adipose tissue (eWAT) than HFD FATP1B+/+ mice. HFD FATP1B-/- also had higher percentage body fat and higher plasma leptin levels when compared to HFD FATP1B+/+ mice. HFD FATP1B-/- had higher expression of inflammatory markers in eWATcompared to HFD FATP1B+/+ mice. The HFD FATP1B-/- group had significantly increased expression of macrophage markers F4/80, CD11C, and CD11B in eWAT. Inflammatory cytokine IL6 was also significantly elevated in eWAT in HFD-fedFATP1B-/- mice when compared to HFD-fed FATP1B+/+ mice. Glucose transporter GLUT1 and components of the Nlrp3 inflammasomeexpressionwere also increased in HFD-fed FATP1B-/- in eWAT. Taken together, these data indicate that deletion of FATP1 from macrophages is associated with increased susceptibility to high fat diet-induced weight gain, increased macrophage adipose tissue infiltration, and a shift towards M1-like pro-inflammatory macrophage polarization. ThusM2 macrophages’ inability to use fatty acids as fuel drove a more pro-inflammatory phenotype. This study reveals insight into a possible mechanism for the polarization of macrophages that may be relevant to the propagation of obesity.Bachelor of Science in Public Healt

Delivery of consistent and high-quality antibody therapeutics by actively monitoring and controlling critical quality attributes

Therapeutic recombinant monoclonal antibodies (mAbs) display a wide variety of critical quality attributes (CQAs) that are essential for achieving their safety and efficacy endpoint in patients. Traditionally, to ensure consistent product quality, manufacturing processes are designed to control CQAs by operating process parameters within defined ranges. This “process defines product” approach has produced many successes within the biopharmaceutical industry, albeit, with limited understanding of the underlying mechanisms between the process parameters and CQAs. Recently, with inclusion of biosimilars and novel modalities into Amgen’s pipeline, meeting tightly specified CQAs using this traditional approach has sometimes proven to be challenging. To better meet such challenges moving forward, we need to develop processes that are adaptable and yet offer robust CQAs control. One strategy for accomplishing this is to develop a product attribute control (PAC) platform that integrates process science with process model control to modulate the CQAs throughout the production processes. PAC is an attribute-focused method that starts by defining desired CQAs and further elucidating the process and attribute relationship (PAR). PAR provides mechanistic understanding of how process parameters (levers) impact CQAs and identifies effective levers that could modulate CQAs of interest within pre-determined ranges. One of the key elements of a PAC process is the integration of process analytical technology (PAT) elements to enact real-time sampling and analytics. Based on real-time process inputs and CQA responses generated by PAT, a mechanistic predictive control model (MPC) or an empirical multivariate statistical process control model (MSPC) for one or more CQAs can be created, and integrated into PAC. In addition, such an approach begins with initial clone selection, with the goal of identifying production cell lines that are responsive to process levers over a dynamic range that will enable adaptive control. This PAC strategy, by combining PAR, PAT, and MPC/MSPC, enables CQAs to be monitored, predicted, and controlled throughout the production process. A study demonstrating control of glycan CQAs incorporating aforementioned PAC strategy will be demonstrated in this presentation. This newly proposed strategy enables robust CQAs control to challenging molecules and ensures the delivery of high quality mAb therapeutics to our patients

A gene selection method for GeneChip array data with small sample sizes

<p>Abstract</p> <p>Background</p> <p>In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods.</p> <p>Results</p> <p>We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate.</p> <p>Conclusion</p> <p>Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations.</p

Ovipositional periodicity of caged Anopheles gambiae individuals

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