Bayesian correlated clustering to integrate multiple datasets.

MOTIVATION: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct-but often complementary-information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. RESULTS: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI's performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques-as well as to non-integrative approaches-demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods.

The morphology of decorated amyloid fibers is controlled by the conformation and position of the displayed protein.

Self-assembled structures capable of mediating electron transfer are an attractive scientific and technological goal. Therefore, systematic variants of SH3-Cytochrome b(562) fusion proteins were designed to make amyloid fibers displaying heme-b(562) electron transfer complexes. TEM and AFM data show that fiber morphology responds systematically to placement of b(562) within the fusion proteins. UV-vis spectroscopy shows that, for the fusion proteins under test, only half the fiber-borne b(562) binds heme with high affinity. Cofactor binding also improves the AFM imaging properties and changes the fiber morphology through changes in cytochrome conformation. Systematic observations and measurements of fiber geometry suggest that longitudinal registry of subfilaments within the fiber, mediated by the interaction and conformation of the displayed proteins and their interaction with surfaces, gives rise to the observed morphologies, including defects and kinks. Of most interest is the role of small molecule modulation of fiber structure and mechanical stability. A minimum complexity model is proposed to capture and explain the fiber morphology in the light of these results. Understanding the complex interplay between these factors will enable a fiber design that supports longitudinal electron transfer.

Spectroscopic characterization of protein-wrapped single-wall carbon nanotubes and quantification of their cellular uptake in multiple cell generations

We study the spectral characteristics of bovine serum albumin (BSA) protein conjugated single-wall carbon nanotubes (SWNTs), and quantify their uptake by macrophages. The binding of BSA onto the SWNT surface is found to change the protein structure and to increase the doping of the nanotubes. The G-band Raman intensity follows a well-defined power law for SWNT concentrations of up to 33 μg ml-1 in aqueous solutions. Subsequently, in vitro experiments demonstrate that incubation of BSA-SWNT complexes with macrophages affects neither the cellular growth nor the cellular viability over multiple cell generations. Using wide spot Raman spectroscopy as a fast, non-destructive method for statistical quantification, we observe that macrophages effectively uptake BSA-SWNT complexes, with the average number of nanotubes internalized per cell remaining relatively constant over consecutive cell generations. The number of internalized SWNTs is found to be ∼30 × 106 SWNTs/cell for a 60 mm-2 seeding density and ∼100 × 10 6 SWNTs/cell for a 200 mm-2 seeding density. Our results show that BSA-functionalized SWNTs are an efficient molecular transport system with low cytotoxicity maintained over multiple cell generations. © 2013 IOP Publishing Ltd.