The mammalian gut harbors complex and variable microbial communities, across both host phylogenetic space and conspecific individuals. A synergy of host genetic and environmental factors shape these communities and account for their variability, but their individual contributions and the selective pressures involved are still not well understood. We employed barcoded pyrosequencing of V1-2 and V4 regions of bacterial small subunit ribosomal RNA genes to characterize the effects of host genetics and environment on cecum assemblages in 10 genetically distinct, inbred mouse strains. Eight of these strains are the foundation of the Collaborative Cross (CC), a panel of mice derived from a genetically diverse set of inbred founder strains, designed specifically for complex trait analysis. Diversity of gut microbiota was characterized by complementing phylogenetic and distance-based, sequence-clustering approaches. Significant correlations were found between the mouse strains and their gut microbiota, reflected by distinct bacterial communities. Cohabitation and litter had a reduced, although detectable effect, and the microbiota response to these factors varied by strain. We identified bacterial phylotypes that appear to be discriminative and strainspecific to each mouse line used. Cohabitation of different strains of mice revealed an interaction of host genetic and environmental factors in shaping gut bacterial consortia, in which bacterial communities became more similar but retained strain specificity. This study provides a baseline analysis of intestinal bacterial communities in the eight CC progenitor strains and will be linked to integrated host genotype, phenotype and microbiota research on the resulting CC panel.

The formation and composition of the protein corona on silica (SiO2) nanoparticles (NP) with different surface chemistries was evaluated over time. Native SiO2, amine (-NH2) and carboxy (-COO(-)) modified NP were examined following incubation in mammalian growth media containing fetal bovine serum (FBS) for 1, 4, 24 and 48 hours. The protein corona transition from its early dynamic state to the later more stable corona was evaluated using mass spectrometry. The NP diameter was 22.4 ± 2.2 nm measured by scanning transmission electron microscopy (STEM). Changes in hydrodynamic diameter and agglomeration kinetics were studied using dynamic light scattering (DLS). The initial surface chemistry of the NP played an important role in the development and final composition of the protein corona, impacting agglomeration kinetics and NP toxicity. Particle toxicity, indicated by changes in membrane integrity and mitochondrial activity, was measured by lactate dehydrogenase (LDH) release and tetrazolium reduction (MTT), respectively, in mouse alveolar macrophages (RAW264.7) and mouse lung epithelial cells (C10). SiO2-COO(-) NP had a slower agglomeration rate, formed smaller aggregates, and exhibited lower cytotoxicity compared to SiO2 and SiO2-NH2. Composition of the protein corona for each of the three NP was unique, indicating a strong dependence of corona development on NP surface chemistry. This work underscores the need to understand all aspects of NP toxicity, particularly the influence of agglomeration on effective dose and particle size. Furthermore, the interplay between materials and local biological environment is emphasized and highlights the need to conduct toxicity profiling under physiologically relevant conditions that provide an appropriate estimation of material modifications that occur during exposure in natural environments.

Predicting the range of substrates accepted by an enzyme from its amino acid sequence is challenging. Although sequence‐ and structure‐based annotation approaches are often accurate for predicting broad categories of substrate specificity, they generally cannot predict which specific molecules will be accepted as substrates for a given enzyme, particularly within a class of closely related molecules. Combining targeted experimental activity data with structural modeling, ligand docking, and physicochemical properties of proteins and ligands with various machine learning models provides complementary information that can lead to accurate predictions of substrate scope for related enzymes. Here we describe such an approach that can predict the substrate scope of bacterial nitrilases, which catalyze the hydrolysis of nitrile compounds to the corresponding carboxylic acids and ammonia. Each of the four machine learning models (logistic regression, random forest, gradient‐boosted decision trees, and support vector machines) performed similarly (average ROC = 0.9, average accuracy = ~82%) for predicting substrate scope for this dataset, although random forest offers some advantages. This approach is intended to be highly modular with respect to physicochemical property calculations and software used for structural modeling and docking.

Cell-free protein synthesis (CFPS) is a powerful technology that allows for optimization of protein production without maintenance of a living system. Integrated within micro and nanofluidic architectures, CFPS can be optimized for point-of-care use. Here, the development of a microfluidic bioreactor designed to facilitate the production of a single-dose of a therapeutic protein, in a small footprint device at the point-of-care, is described. This new design builds on the use of a long, serpentine channel bioreactor and is enhanced by integrating a nanofabricated membrane to allow exchange of materials between parallel "reactor" and "feeder" channels. This engineered membrane facilitates the exchange of metabolites, energy, and inhibitory species, and can be altered by plasma-enhanced chemical vapor deposition and atomic layer deposition to tune the exchange rate of small molecules. This allows for extended reaction times and improved yields. Further, the reaction product and higher molecular weight components of the transcription/translation machinery in the reactor channel can be retained. It has been shown that the microscale bioreactor design produces higher protein yields than conventional tube-based batch formats, and that product yields can be dramatically improved by facilitating small molecule exchange within the dual-channel bioreactor.

Light chain (AL) amyloidosis is the most common form of systemic amyloid disease, and cardiomyopathy is a dire consequence, resulting in an extremely poor prognosis. AL is characterized by the production of monoclonal free light chains that deposit as amyloid fibrils principally in the heart, liver, and kidneys causing organ dysfunction. We have studied the effects of amyloid fibrils, produced from recombinant λ6 light chain variable domains, on metabolic activity of human cardiomyocytes. The data indicate that fibrils at 0.1 μM, but not monomer, significantly decrease the enzymatic activity of cellular NAD(P)H-dependent oxidoreductase, without causing significant cell death. The presence of amyloid fibrils did not affect ATP levels; however, oxygen consumption was increased and reactive oxygen species were detected. Confocal fluorescence microscopy showed that fibrils bound to and remained at the cell surface with little fibril internalization. These data indicate that AL amyloid fibrils severely impair cardiomyocyte metabolism in a dose dependent manner. These data suggest that effective therapeutic intervention for these patients should include methods for removing potentially toxic amyloid fibrils.