Reconstructing visual experiences from brain activity evoked by natural movies

Quantitative modeling of human brain activity can provide crucial insights about cortical representations [1, 2] and can form the basis for brain decoding devices [3-5]. Recent functional magnetic resonance imaging (fMRI) studies have modeled brain activity elicited by static visual patterns and have reconstructed these patterns from brain activity [6-8]. However, blood oxygen level-dependent (BOLD) signals measured via fMRI are very slow [9], so it has been difficult to model brain activity elicited by dynamic stimuli such as natural movies. Here we present a new motion-energy [10, 11] encoding model that largely overcomes this limitation. The model describes fast visual information and slow hemodynamics by separate components. We recorded BOLD signals in occipitotemporal visual cortex of human subjects who watched natural movies and fit the model separately to individual voxels. Visualization of the fit models reveals how early visual areas represent the information in movies. To demonstrate the power of our approach, we also constructed a Bayesian decoder [8] by combining estimated encoding models with a sampled natural movie prior. The decoder provides remarkable reconstructions of the viewed movies. These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.     

Aromatic aldehyde and hydrazine activated peptide coated quantum dots for easy bioconjugation and live cell imaging

We present a robust scheme for preparation of semiconductor quantum dots (QDs) and cognate partners in a conjugation ready format. Our approach is based on bis-aryl hydrazone bond formation mediated by aromatic aldehyde and hydrazinonicotinate acetone hydrazone (HyNic) activated peptide coated quantum dots. We demonstrate controlled preparation of antibody--QD bioconjugates for specific targeting of endogenous epidermal growth factor receptors in breast cancer cells and for single QD tracking of transmembrane proteins via an extracellular epitope. The same approach was also used for optical mapping of RNA polymerases bound to combed genomic DNA in vitro.     

Robust control of nitrogen assimilation by a bifunctional enzyme in E. coli

Bacteria regulate the assimilation of multiple nutrients to enable growth. How is balanced utilization achieved, despite fluctuations in the concentrations of the enzymes that make up the regulatory circuitry? Here we address this question by studying the nitrogen system of E. coli. A mechanism based on the avidity of a bifunctional enzyme, adenylyltransferase (AT/AR), to its multimeric substrate, glutamine synthetase, is proposed to maintain a robust ratio between two key metabolites, glutamine and α-ketoglutarate. This ratio is predicted to be insensitive to variations in protein levels of the core circuit and to the rate of nitrogen utilization. We find using mass spectrometry that the metabolite ratio is robust to variations in protein levels and that this robustness depends on the bifunctional enzyme. Moreover, robustness carries through to the bacteria growth rate. Interrupting avidity by adding a monofunctional AT/AR mutant to the native system abolishes robustness, as predicted by the proposed mechanism.     

Glycoside hydrolases as components of putative carbohydrate biosensor proteins in <Emphasis Type="Italic">Clostridium thermocellum</Emphasis>

The composition of the cellulase system in the cellulosome-producing bacterium, Clostridium thermocellum, has been reported to change in response to growth on different carbon sources. Recently, an extensive carbohydrate-sensing mechanism, purported to regulate the activation of genes coding for polysaccharide-degrading enzymes, was suggested. In this system, CBM modules, comprising extracellular components of RsgI-like anti-σ factors, were proposed to function as carbohydrate sensors, through which a set of cellulose utilization genes are activated by the associated σ^I-like factors. An extracellular module of one of these RsgI-like proteins (Cthe_2119) was annotated as a family 10 glycoside hydrolase, RsgI6-GH10, and a second putative anti-σ factor (Cthe_1471), related in sequence to Rsi24, was found to contain a module that resembles a family 5 glycoside hydrolase (termed herein Rsi24C-GH5). The present study examines the relevance of these two glycoside hydrolases as sensors in this signal-transmission system. The RsgI6-GH10 was found to bind xylan matrices but exhibited low enzymatic activity on this substrate. In addition, this glycoside hydrolase module was shown to interact with crystalline cellulose although no hydrolytic activity was detected on cellulosic substrates. Bioinformatic analysis of the Rsi24C-GH5 showed a glutamate-to-glutamine substitution that would presumably preclude catalytic activity. Indeed, the recombinant module was shown to bind to cellulose, but showed no hydrolytic activity. These observations suggest that these two glycoside hydrolases underwent an evolutionary adaptation to function as polysaccharide binding agents rather than enzymatic components and thus serve in the capacity of extracellular carbohydrate sensors.     

VDA, a method of choosing a better algorithm with fewer validations

The multitude of bioinformatics algorithms designed for performing a particular computational task presents end-users with the problem of selecting the most appropriate computational tool for analyzing their biological data. The choice of the best available method is often based on expensive experimental validation of the results. We propose an approach to design validation sets for method comparison and performance assessment that are effective in terms of cost and discrimination power.Validation Discriminant Analysis (VDA) is a method for designing a minimal validation dataset to allow reliable comparisons between the performances of different algorithms. Implementation of our VDA approach achieves this reduction by selecting predictions that maximize the minimum Hamming distance between algorithmic predictions in the validation set. We show that VDA can be used to correctly rank algorithms according to their performances. These results are further supported by simulations and by realistic algorithmic comparisons in silico.VDA is a novel, cost-efficient method for minimizing the number of validation experiments necessary for reliable performance estimation and fair comparison between algorithms.Our VDA software is available at http://sourceforge.net/projects/klugerlab/files/VDA/     

Cross talk between gibberellin and cytokinin: the Arabidopsis GA response inhibitor SPINDLY plays a positive role in cytokinin signaling

SPINDLY (SPY) is a negative regulator of gibberellin (GA) responses; however, spy mutants exhibit various phenotypic alterations not found in GA-treated plants. Assaying for additional roles for SPY revealed that spy mutants are resistant to exogenously applied cytokinin. GA also repressed the effects of cytokinin, suggesting that there is cross talk between the two hormone-response pathways, which may involve SPY function. Two spy alleles showing severe (spy-4) and mild (spy-3) GA-associated phenotypes exhibited similar resistance to cytokinin, suggesting that SPY enhances cytokinin responses and inhibits GA signaling through distinct mechanisms. GA and spy repressed numerous cytokinin responses, from seedling development to senescence, indicating that cross talk occurs early in the cytokinin-signaling pathway. Because GA3 and spy-4 inhibited induction of the cytokinin primary-response gene, type-A Arabidopsis response regulator 5, SPY may interact with and modify elements from the phosphorelay cascade of the cytokinin signal transduction pathway. Cytokinin, on the other hand, had no effect on GA biosynthesis or responses. Our results demonstrate that SPY acts as both a repressor of GA responses and a positive regulator of cytokinin signaling. Hence, SPY may play a central role in the regulation of GA/cytokinin cross talk during plant development.     

Matching fusion protein systems for affinity analysis of two interacting families of proteins: the cohesin-dockerin interaction

Cellulosomes are multi-enzyme complexes that orchestrate the efficient degradation of cellulose and related plant cell wall polysaccharides. The complex is maintained by the high-affinity protein-protein interaction between two complementary modules: the cohesin and the dockerin. In order to characterize the interaction between different cohesins and dockerins, we have developed matching fusion-protein systems, which harbor either the cohesin or the dockerin component. For this purpose, corresponding plasmid cassettes were designed, which encoded for the following carrier proteins: (i) a thermostable xylanase with an appended His-tag; and (ii) a highly stable cellulose-binding module (CBM). The resultant xylanase-dockerin and CBM-cohesin fusion products exhibited high expression levels of soluble protein. The expressed, affinity-purified proteins were extremely stable, and the functionality of the cohesin or dockerin component was retained. The fusion protein system was used to establish a sensitive and reliable, semi-quantitative enzyme-linked affinity assay for determining multiple samples of cohesin-dockerin interactions in microtiter plates. A variety of cohesin-dockerin systems, which had been examined previously using other methodologies, were revisited applying the affinity-based enzyme assay, the results of which served to verify the validity of the approach.     

Approaching the CAPRI challenge with an efficient geometry-based docking

The last 3 rounds (3-5) of CAPRI included a wide range of docking targets. Several targets were especially challenging, since they involved large-scale movements and symmetric rearrangement, while others were based on homology models. We have approached the targets with a variety of geometry-based docking algorithms that include rigid docking, symmetric docking, and flexible docking with symmetry constraints. For all but 1 docking target, we were able to submit at least 1 acceptable quality prediction. Here, we detail for each target the prediction methods used and the specific biological data employed, and supply a retrospective analysis of the results. We highlight the advantages of our techniques, which efficiently exploit the geometric shape complementarity properties of the interaction. These enable them to run only few minutes on a standard PC even for flexible docking, thus proving their scalability toward computational genomic scale experiments. We also outline the major required enhancements, such as the introduction of side-chain position refinement and the introduction of flexibility for both docking partners.