Excited-state reaction dynamics of bacteriorhodopsin studied by femtosecond spectroscopy

The photodynamics of bacteriorhodopsin were studied by transient absorption and gain measurements after excitation with femtosecond pulses at 620 nm. With probing pulses at longer wavelengths (λ > 770 nm) the previously reported formation of the J intermediate (with a time constant of 500±100 fs) was confirmed. With probing pulses around 700 nm, a faster process with a relaxation time of 200±70 fs was observed. The data analysis strongly suggests that this kinetic constant describes the reactive motion of the polyatomic molecule on its excited-state potential energy surface, i.e. one observes directly the incipient isomerization of the retinal molecule. The minimum of the S1 potential energy surface reached in 200 fs lies approximately 13300 cm−1 above the ground state of bacteriorhodopsin and from this minimum the intermediate J is formed with a time constant of 500 fs

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 via parameters that describe the agreement among the datasets. Results: Using a set of 6 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 S. cerevisiae datasets. In the 2-dataset case, we show that MDI’s performance is comparable to the present state of the art. We then move beyond the capabilities of current approaches and integrate gene expression, ChIP-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 very competitive, while also providing information that would be difficult or impossible to extract using other methods

The zinc cluster protein Sut1 contributes to filamentation in Saccharomyces cerevisiae

Copyright © 2013, American Society for Microbiology. All Rights ReservedSut1 is a transcriptional regulator of the Zn(II)(2)Cys(6) family in the budding yeast Saccharomyces cerevisiae. The only function that has been attributed to Sut1 is sterol uptake under anaerobic conditions. Here, we show that Sut1 is also expressed in the presence of oxygen, and we identify a novel function for Sut1. SUT1 overexpression blocks filamentous growth, a response to nutrient limitation, in both haploid and diploid cells. This inhibition by Sut1 is independent of its function in sterol uptake. Sut1 downregulates the expression of GAT2, HAP4, MGA1, MSN4, NCE102, PRR2, RHO3, and RHO5. Several of these Sut1 targets (GAT2, HAP4, MGA1, RHO3, and RHO5) are essential for filamentation in haploids and/or diploids. Furthermore, the expression of the Sut1 target genes, with the exception of MGA1, is induced during filamentous growth. We also show that SUT1 expression is autoregulated and inhibited by Ste12, a key transcriptional regulator of filamentation. We propose that Sut1 partially represses the expression of GAT2, HAP4, MGA1, MSN4, NCE102, PRR2, RHO3, and RHO5 when nutrients are plentiful. Filamentation-inducing conditions relieve this repression by Sut1, and the increased expression of Sut1 targets triggers filamentous growth.The project was supported by Deutsche Forschungsgemeinschaft grant HO 2098/

Assessing the functional structure of genomic data

Motivation: The availability of genome-scale data has enabled an abundance of novel analysis techniques for investigating a variety of systems-level biological relationships. As thousands of such datasets become available, they provide an opportunity to study high-level associations between cellular pathways and processes. This also allows the exploration of shared functional enrichments between diverse biological datasets, and it serves to direct experimenters to areas of low data coverage or with high probability of new discoveries

GraphWeb: mining heterogeneous biological networks for gene modules with functional significance

Deciphering heterogeneous cellular networks with embedded modules is a great challenge of current systems biology. Experimental and computational studies construct complex networks of molecules that describe various aspects of the cell such as transcriptional regulation, protein interactions and metabolism. Groups of interacting genes and proteins reflect network modules that potentially share regulatory mechanisms and relate to common function. Here, we present GraphWeb, a public web server for biological network analysis and module discovery. GraphWeb provides methods to: (1) integrate heterogeneous and multispecies data for constructing directed and undirected, weighted and unweighted networks; (ii) discover network modules using a variety of algorithms and topological filters and (iii) interpret modules using functional knowledge of the Gene Ontology and pathways, as well as regulatory features such as binding motifs and microRNA targets. GraphWeb is designed to analyse individual or multiple merged networks, search for conserved features across multiple species, mine large biological networks for smaller modules, discover novel candidates and connections for known pathways and compare results of high-throughput datasets. The GraphWeb is available at http://biit.cs.ut.ee/graphweb/

Integrating Phosphorylation Network with Transcriptional Network Reveals Novel Functional Relationships

Phosphorylation and transcriptional regulation events are critical for cells to transmit and respond to signals. In spite of its importance, systems-level strategies that couple these two networks have yet to be presented. Here we introduce a novel approach that integrates the physical and functional aspects of phosphorylation network together with the transcription network in S.cerevisiae, and demonstrate that different network motifs are involved in these networks, which should be considered in interpreting and integrating large scale datasets. Based on this understanding, we introduce a HeRS score (hetero-regulatory similarity score) to systematically characterize the functional relevance of kinase/phosphatase involvement with transcription factor, and present an algorithm that predicts hetero-regulatory modules. When extended to signaling network, this approach confirmed the structure and cross talk of MAPK pathways, inferred a novel functional transcription factor Sok2 in high osmolarity glycerol pathway, and explained the mechanism of reduced mating efficiency upon Fus3 deletion. This strategy is applicable to other organisms as large-scale datasets become available, providing a means to identify the functional relationships between kinases/phosphatases and transcription factors

Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.National Science Foundation (U.S.) (DB1-0821391)National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-GM089903)National Institutes of Health (U.S.) (P30-ES002109

Education Manpower and Economic Growth

xiii.229hal;ill.;21 c

Education, mapower and economic growth strategies of human resourve development

ix, 229 p.; 21 cm