Prediction analysis for microbiome sequencing data

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension‐reduction structure in the model. An efficient Monte Carlo expectation‐maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.     

The pearl necklace model in protein A chromatography: Molecular mechanisms at the resin interface

Staphylococcal protein A chromatography is an established core technology for monoclonal antibody purification and capture in the downstream processing. MabSelect SuRe involves a tetrameric chain of a recombinant form of the B domain of staphylococcal protein A, called the Z-domain. Little is known about the stoichiometry, binding orientation, or preferred binding. We analyzed small-angle X-ray scattering data of the antibody–protein A complex immobilized in an industrial highly relevant chromatographic resin at different antibody concentrations. From scattering data, we computed the normalized radial density distributions. We designed three-dimensional (3D) models with protein data bank crystallographic structures of an IgG1 (the isoform of trastuzumab, used here; Protein Data Bank: 1HZH) and the staphylococcal protein A B domain (the native form of the recombinant structure contained in MabSelect SuRe resin; Protein Data Bank: 1BDD). We computed different binding conformations for different antibody to protein A stoichiometries (1:1, 2:1, and 3:1) and compared the normalized radial density distributions computed from 3D models with those obtained from the experimental data. In the linear range of the isotherm we favor a 1:1 ratio, with the antibody binding to the outer domains in the protein A chain at very low and high concentrations. In the saturation region, a 2:1 ratio is more likely to occur. A 3:1 stoichiometry is excluded because of steric effects.     

Local and long range potentials for heparin-protein systems for coarse-grained simulations

Heparin belongs to glycosaminoglycans (GAGs), a class of periodic linear anionic polysaccharides, which are functionally important components of the extracellular matrix owing to their interactions with various protein targets. Heparin is known to be involved in many cell signaling processes, while the experimental data available for heparin are significantly more abundant than for other GAGs. At the same time, the length and conformational flexibility of the heparin represent major challenges for its theoretical analysis. Coarse-grained (CG) approaches, which enable us to extend the size- and time-scale by orders of magnitude owing to reduction of system representation, appear, therefore, to be useful in simulating these systems. In this work, by using umbrella-sampling molecular dynamics simulations, we derived and parameterized the CG backbone-local potentials of heparin chains and the orientational potentials for the interactions of heparin with amino acid side chains to be further included in the physics-based Unified Coarse-Grained Model of biological macromolecules. With these potentials, simulations of extracellular matrix processes where both heparin and multiple proteins participate will be possible.     

Dynamic investigation and modeling of the nitrogen cometabolism in Methylococcus capsulatus ( Bath)

The methanotrophic bacterium Methylococcus capsulatus is capable of assimilating methane and oxygen into protein-rich biomass, however, the diverse metabolism of the microorganism also allows for several undesired cometabolic side-reactions to occur. In this study, the ammonia cometabolism in Methylococcus capsulatus is investigated using pulse experiments. Surprisingly Methylococcus capsulatus oxidizes ammonia to nitrate through a yet unknown mechanism and fixes molecular nitrogen even at a high dissolved oxygen tension. The observed phenomena can be modeled using 14 ordinary differential equations and 18 kinetic parameters, of which 6 were revealed by Morris screening to be identifiable from the experimental data. Monte Carlo simulations showed that the model was robust and accurate even with uncertainty in the parameter values as confirmed by statistical error analysis.     

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.     

Construction of a sensitive pyrogen-testing cell model by site-specific knock-in of multiple genes

A pyrogen test is crucial for evaluating the safety of drugs and medical equipment, especially those involved in injections. As existing pyrogen tests, including the rabbit pyrogen test, the limulus amoebocyte lysate (LAL) test and the monocyte activation test have limitations, development of new models for pyrogen testing is necessary. Here we develop a sensitive cell model for pyrogen test based on the lipopolysaccharides (LPS) signal pathway. TLR4, MD2, and CD14 play key roles in the LPS-mediated pyrogen reaction. We established a new TLR4/MD2/CD14-specific overexpressing knock-in cell model using the CRISPR/CAS9 technology and homologous recombination to detect LPS. Stimulation of our TLR4/CD14/MD2 knock-in cell line model with LPS leads to the release of the cytokines IL-6 and TNF-alpha, with a detection limit of 0.005 EU/ml, which is greatly lower than the lower limit of 0.015 EU/ml detected by the Tachypleus amebocyte lysate (TAL) assay.     

The effects of buffer condition on the fouling behavior of MVM virus filtration of an Fc-fusion protein

A combined pore blockage and cake filtration model was applied to the virus filtration of an Fc-fusion protein using the three commercially available filters, F-1, F-2, and F-3 in a range of buffer conditions including sodium-phosphate and tris-acetate buffers with and without 200 mM NaCl at pH 7.5. The fouling behaviors of the three filters for the feed solutions spiked with minute virus of mice were described well by this combined model for all the solution conditions. This suggests that fouling of the virus filters is dominated by the pore blockage mechanism during the initial stage of the filtration and transformed to the cake filtration mechanism during the later stage of the filtration. Both flux and transmembrane resistance can be described well by this model. The pore blockage rate and the rate of increase of protein layer resistance over blocked pores are found to be affected by membrane properties as well as the solution conditions resulting from the modulation of interactions between virus, protein, and membrane by the solution conditions.     

Local FK506 dose-dependent study using a novel three-dimensional organotypic assay

Local administration of FK506, an FDA approved immunosuppressant with neuroregenerative properties, is a promising technique to achieve improved peripheral nerve regeneration while preventing the side effects associated with the systemic administration of this drug. Although considerable research has been devoted to the development of clinically suitable systems for local delivery of FK506 to the site of nerve injury and repair, the optimal dose of FK506 for enhancement of axon regeneration in the peripheral nerve has not yet been established. To this end, we devised a three-dimensional (3D) organotypic assay capable of mimicking the peripheral nerve. This assay consisted of a neonatal rat dorsal root ganglion (DRG) extending its neurites into the native peripheral nerve scaffold provided by an acellular nerve allograft (ANA). A novel 3D compartmented cell culture system was adapted from the 3D organotypic assay to achieve local delivery of FK506 just to the growing neurites in vitro and establish the required local dose of FK506 for peripheral nerve regeneration. A bimodal dose response was observed by culturing the entire DRG–ANA construct with media containing different concentrations of FK506. Low drug concentration of 1 pg/ml and high drug concentration of 100 ng/ml lead to the longest neurite extension in vitro. Furthermore, regardless of the FK506 concentration, concentrating the drug to the growing neurites resulted in significant increase in both neurite extension and neurite density, an effect that was not observed with the FK506 delivery to both neurites and neural cell bodies within DRG. The findings in this study provide valuable insight into the optimal local dose of FK506 for peripheral nerve regeneration. Furthermore, for the first time, this study suggests the potential interaction of FK506 with axons at the level of the growth cone.     

In silico strategies to couple production of bioethanol with growth in cyanobacteria

Cyanobacteria have been considered as promising candidates for sustainable bioproduction from inexpensive raw materials, as they grow on light, carbon dioxide, and minimal inorganic nutrients. In this study, we present a genome-scale metabolic network model for Synechocystis sp. PCC 6803 and study the optimal design of the strain for ethanol production by using a mixed integer linear problem reformulation of a bilevel programming problem that identifies gene knockouts which lead to coupling between growth and product synthesis. Five mutants were found, where the in silico model predicts coupling between biomass growth and ethanol production in photoautotrophic conditions. The best mutant gives an in silico ethanol production of 1.054 mmol·gDW ⁻¹·h ⁻¹.