Deep sequencing of the zebrafish transcriptome response to mycobacterium infection.

Novel high-throughput deep sequencing technology has dramatically changed the way that the functional complexity of transcriptomes can be studied. Here we report on the first use of this technology to gain insight into the wide range of transcriptional responses that are associated with an infectious disease process. Using Solexa/Illumina's digital gene expression (DGE) system, a tag-based transcriptome sequencing method, we investigated mycobacterium-induced transcriptome changes in a model vertebrate species, the zebrafish. We obtained a sequencing depth of over 5 million tags per sample with strong correlation between replicates. Tag mapping indicated that healthy and infected adult zebrafish express over 70% of all genes represented in transcript databases. Comparison of the data with a previous multi-platform microarray analysis showed that both types of technologies identified regulation of similar functional groups of genes. However, the unbiased nature of DGE analysis provided insights that microarray analysis could not have achieved. In particular, we show that DGE data sets are instrumental for verification of predicted gene models and allowed us to detect mycobacterium-regulated switching between different transcript isoforms. Moreover, genomic mapping of infection-induced DGE tags revealed novel transcript forms for which any previous EST-based evidence of expression was lacking. In conclusion, our deep sequencing analysis revealed in depth the high degree of transcriptional complexity of the host response to mycobacterial infection and resulted in the discovery and validation of new gene products with induced expression in infected individuals.

Multiple weak hits confuse complex systems: a transcriptional regulatory network as an example.

Robust systems, like the molecular networks of living cells, are often resistant to single hits such as those caused by high-specificity drugs. Here we show that partial weakening of the Escherichia coli and Saccharomyces cerevisiae transcriptional regulatory networks at a small number (3-5) of selected nodes can have a greater impact than the complete elimination of a single selected node. In both cases, the targeted nodes have the greatest possible impact; still, the results suggest that in some cases broader specificity compounds or multitarget drug therapies may be more effective than individual high-affinity, high-specificity ones. Multiple but partial attacks mimic well a number of in vivo scenarios and may be useful in the efficient modification of other complex systems.

The efficiency of multi-target drugs: the network approach might help drug design.

Despite considerable progress in genome- and proteome-based high-throughput screening methods and rational drug design, the number of successful single-target drugs did not increase appreciably during the past decade. Network models suggest that partial inhibition of a surprisingly small number of targets can be more efficient than the complete inhibition of a single target. This and the success stories of multi-target drugs and combinatorial therapies led us to suggest that systematic drug-design strategies should be directed against multiple targets. We propose that the final effect of partial, but multiple, drug actions might often surpass that of complete drug action at a single target. The future success of this novel drug-design paradigm will depend not only on a new generation of computer models to identify the correct multiple targets and their multi-fitting, low-affinity drug candidates but also on more-efficient in vivo testing.

Graph-representation of oxidative folding pathways.

BACKGROUND: The process of oxidative folding combines the formation of native disulfide bond with conformational folding resulting in the native three-dimensional fold. Oxidative folding pathways can be described in terms of disulfide intermediate species (DIS) which can also be isolated and characterized. Each DIS corresponds to a family of folding states (conformations) that the given DIS can adopt in three dimensions. RESULTS: The oxidative folding space can be represented as a network of DIS states interconnected by disulfide interchange reactions that can either create/abolish or rearrange disulfide bridges. We propose a simple 3D representation wherein the states having the same number of disulfide bridges are placed on separate planes. In this representation, the shuffling transitions are within the planes, and the redox edges connect adjacent planes. In a number of experimentally studied cases (bovine pancreatic trypsin inhibitor, insulin-like growth factor and epidermal growth factor), the observed intermediates appear as part of contiguous oxidative folding pathways. CONCLUSIONS: Such networks can be used to visualize folding pathways in terms of the experimentally observed intermediates. A simple visualization template written for the Tulip package http://www.tulip-software.org/ can be obtained from V.A.

The SBASE domain sequence resource, release 12: prediction of protein domain-architecture using support vector machines.

SBASE (http://www.icgeb.trieste.it/sbase) is an online resource designed to facilitate the detection of domain homologies based on sequence database search. The present release of the SBASE A library of protein domain sequences contains 972,397 protein sequence segments annotated by structure, function, ligand-binding or cellular topology, clustered into 8547 domain groups. SBASE B contains 169,916 domain sequences clustered into 2526 less well-characterized groups. Domain prediction is based on an evaluation of database search results in comparison with a 'similarity network' of inter-sequence similarity scores, using support vector machines trained on similarity search results of known domains.