Amyloid-ß concentration and structure influences the transport and immunomodulatory effects of IVIG.

Intravenous immunoglobulin (IVIG) contains anti-amyloid-ß antibodies as well as antibodies providing immunomodulatory effects that may modify chronic inflammation in Alzheimer's disease. Answers to important questions about IVIG transport into the central nervous system and assessments of any impact amyloid-ß has on this transport can be provided by in vitro models of the blood-brain barrier. In this study, amyloid-ß[1-42] was pre-aggregated into fibrillar or oligomeric structures, and various concentrations were incubated in the brain side of the blood-brain barrier model, followed by IVIG administration in the blood side at the therapeutically relevant concentrations of 5 and 20 mg/mL. IVIG accumulated in the brain side at physiologically relevant levels, with amyloid-ß pre-incubation increasing IVIG accumulation. The increased transport effect was dependent on amyloid-ß structural form, amyloid-ß concentration, and IVIG dose. IVIG was found to decrease monocyte chemotactic protein-1 levels 6.5-18% when low amyloid-ß levels were present and increase levels 4.2-23% when high amyloid-ß levels were present. Therefore, the presence, concentration, and structure of amyloid-ß plays an important role in the effect of IVIG therapy in the brain. The mechanisms of action and transport across the blood-brain barrier (BBB) for an Alzheimer's disease therapeutic, intravenous immunoglobulin (IVIG), remain unknown. We investigated the transport of IVIG across endothelial cell BBB monolayers pre-incubated with amyloid-ß peptides. We found that the concentration and structure of amyloid-ß plays an important role in the effect of IVIG on BBB tightening and cytokine neutralization. (Note: Figure not drawn to scale.).

Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation.

BACKGROUND: Better understanding and prediction of progression of Parkinson's disease could improve disease management and clinical trial design. We aimed to use longitudinal clinical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and identify novel predictors for motor progression in Parkinson's disease. We also sought to assess the use of these models in the design of treatment trials in Parkinson's disease. METHODS: A Bayesian multivariate predictive inference platform was applied to data from the Parkinson's Progression Markers Initiative (PPMI) study (NCT01141023). We used genetic data and baseline molecular and clinical variables from patients with Parkinson's disease and healthy controls to construct an ensemble of models to predict the annual rate of change in combined scores from the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) parts II and III. We tested our overall explanatory power, as assessed by the coefficient of determination (R2), and replicated novel findings in an independent clinical cohort from the Longitudinal and Biomarker Study in Parkinson's disease (LABS-PD; NCT00605163). The potential utility of these models for clinical trial design was quantified by comparing simulated randomised placebo-controlled trials within the out-of-sample LABS-PD cohort. FINDINGS: 117 healthy controls and 312 patients with Parkinson's disease from the PPMI study were available for analysis, and 317 patients with Parkinson's disease from LABS-PD were available for validation. Our model ensemble showed strong performance within the PPMI cohort (five-fold cross-validated R2 41%, 95% CI 35-47) and significant-albeit reduced-performance in the LABS-PD cohort (R2 9%, 95% CI 4-16). Individual predictive features identified from PPMI data were confirmed in the LABS-PD cohort. These included significant replication of higher baseline MDS-UPDRS motor score, male sex, and increased age, as well as a novel Parkinson's disease-specific epistatic interaction, all indicative of faster motor progression. Genetic variation was the most useful predictive marker of motor progression (2·9%, 95% CI 1·5-4·3). CSF biomarkers at baseline showed a more modest (0·3%, 95% CI 0·1-0·5) but still significant effect on prediction of motor progression. The simulations (n=5000) showed that incorporating the predicted rates of motor progression (as assessed by the annual change in MDS-UPDRS score) into the final models of treatment effect reduced the variability in the study outcome, allowing significant differences to be detected at sample sizes up to 20% smaller than in naive trials. INTERPRETATION: Our model ensemble confirmed established and identified novel predictors of Parkinson's disease motor progression. Improvement of existing prognostic models through machine-learning approaches should benefit trial design and evaluation, as well as clinical disease monitoring and treatment. FUNDING: Michael J Fox Foundation for Parkinson's Research and National Institute of Neurological Disorders and Stroke.

Membrane configuration optimization for a murine in vitro blood-brain barrier model.

A powerful experimental tool used to study the dynamic functions of the blood-brain barrier (BBB) is an in vitro cellular based system utilizing cell culture inserts in multi-well plates. Currently, usage of divergent model configurations without explanation of selected variable set points renders data comparisons difficult and limits widespread understanding. This work presents for the first time in literature a comprehensive screening study to optimize membrane configuration, with aims to unveil influential membrane effects on the ability of cerebral endothelial cells to form a tight monolayer. First, primary murine brain endothelial cells and astrocytes were co-cultured in contact and non-contact orientations on membranes of pore diameter sizes ranging from 0.4 µm to 8.0 µm, and the non-contact orientation and smallest pore diameter size were shown to support a significantly tighter monolayer formation. Then, membranes made from polyethylene terephthalate (PET) and polycarbonate (PC) purchased from three different commercial sources were compared, and PET membranes purchased from two manufacturers facilitated a significantly tighter monolayer formation. Models were characterized by transendothelial electrical resistance (TEER), sodium fluorescein permeability, and immunocytochemical labeling of tight junction proteins. Finally, a murine brain endothelial cell line, bEnd.3, was grown on the different membranes, and similar results were obtained with respect to optimal membrane configuration selection. The results and methodology presented here on high throughput 24-well plate inserts can be translated to other BBB systems to advance model understanding.

Optimization of endothelial cell growth in a murine in vitro blood-brain barrier model.

In vitro cell culture models of the blood-brain barrier (BBB) are important tools used to study cellular physiology and brain disease therapeutics. Although the number of model configurations is expanding across neuroscience laboratories, it is not clear that any have been effectively optimized. A sequential screening study to identify optimal primary mouse endothelial cell parameter set points, grown alone and in combination with common model enhancements, including co-culturing with primary mouse or rat astrocytes and addition of biochemical agents in the media, was performed. A range of endothelial cell-seeding densities (1-8 × 10(5) cells/cm(2) ) and astrocyte-seeding densities (2-8 × 10(4) cells/cm(2) ) were studied over seven days in the system, and three distinct media-feeding strategies were compared to optimize biochemical agent exposure time. Implementation of all optimal set points increased transendothelial electrical resistance by over 200% compared to an initial model and established a suitable in vitro model for brain disease application studies. These results demonstrate the importance of optimizing cell culture growth, which is the most important parameter in creating an in vitro BBB model as it directly relates the model to the in vivo arrangement.

Genomics in mammalian cell culture bioprocessing.

Explicitly identifying the genome of a host organism including sequencing, mapping, and annotating its genetic code has become a priority in the field of biotechnology with aims at improving the efficiency and understanding of cell culture bioprocessing. Recombinant protein therapeutics, primarily produced in mammalian cells, constitute a $108 billion global market. The most common mammalian cell line used in biologic production processes is the Chinese hamster ovary (CHO) cell line, and although great improvements have been made in titer production over the past 25 years, the underlying molecular and physiological factors are not well understood. Confident understanding of CHO bioprocessing elements (e.g. cell line selection, protein production, and reproducibility of process performance and product specifications) would significantly improve with a well understood genome. This review describes mammalian cell culture use in bioprocessing, the importance of obtaining CHO cell line genetic sequences, and the current status of sequencing efforts. Furthermore, transcriptomic techniques and gene expression tools are presented, and case studies exploring genomic techniques and applications aimed to improve mammalian bioprocess performance are reviewed. Finally, future implications of genomic advances are surmised.