HabiSign: a novel approach for comparison of metagenomes and rapid identification of habitat-specific sequences.

BACKGROUND: One of the primary goals of comparative metagenomic projects is to study the differences in the microbial communities residing in diverse environments. Besides providing valuable insights into the inherent structure of the microbial populations, these studies have potential applications in several important areas of medical research like disease diagnostics, detection of pathogenic contamination and identification of hitherto unknown pathogens. Here we present a novel and rapid, alignment-free method called HabiSign, which utilizes patterns of tetra-nucleotide usage in microbial genomes to bring out the differences in the composition of both diverse and related microbial communities. RESULTS: Validation results show that the metagenomic signatures obtained using the HabiSign method are able to accurately cluster metagenomes at biome, phenotypic and species levels, as compared to an average tetranucleotide frequency based approach and the recently published dinucleotide relative abundance based approach. More importantly, the method is able to identify subsets of sequences that are specific to a particular habitat. Apart from this, being alignment-free, the method can rapidly compare and group multiple metagenomic data sets in a short span of time. CONCLUSIONS: The proposed method is expected to have immense applicability in diverse areas of metagenomic research ranging from disease diagnostics and pathogen detection to bio-prospecting. A web-server for the HabiSign algorithm is available at http://metagenomics.atc.tcs.com/HabiSign/.

When parameters in dynamic models become phenotypes: a case study on flesh pigmentation in the chinook salmon (Oncorhynchus tshawytscha).

The Pacific chinook salmon occurs as both white- and red-fleshed populations, with the flesh color type (red or white) seemingly under strong genetic influence. Previously published data on crosses between red- and white-fleshed individuals cannot be reconciled with a simple Mendelian two-locus, two-allele model, pointing to either a more complex inheritance pattern or the existence of gene interactions. Here we show that a standard single-locus, three-allele model can fully explain these data. Moreover, by implementing the single-locus model at the parameter level of a previously developed mathematical model describing carotenoid dynamics in salmon, we show that variation at a single gene involved in the muscle uptake of carotenoids is able to explain the available data. This illustrates how such a combined approach can generate biological understanding that would not be possible in a classical population genetic explanatory structure. An additional asset of this approach is that by allowing parameters to become phenotypes obeying a given genetic model, biological interpretations of mechanisms involved at a resolution level far beyond what is built into the original dynamic model are made possible. These insights can in turn be exploited in experimental studies as well as in construction of more detailed models.

Carotenoid dynamics in Atlantic salmon.

BACKGROUND: Carotenoids are pigment molecules produced mainly in plants and heavily exploited by a wide range of organisms higher up in the food-chain. The fundamental processes regulating how carotenoids are absorbed and metabolized in vertebrates are still not fully understood. We try to further this understanding here by presenting a dynamic ODE (ordinary differential equation) model to describe and analyse the uptake, deposition, and utilization of a carotenoid at the whole-organism level. The model focuses on the pigment astaxanthin in Atlantic salmon because of the commercial importance of understanding carotenoid dynamics in this species, and because deposition of carotenoids in the flesh is likely to play an important life history role in anadromous salmonids. RESULTS: The model is capable of mimicking feed experiments analyzing astaxanthin uptake and retention over short and long time periods (hours, days and years) under various conditions. A sensitivity analysis of the model provides information on where to look for possible genetic determinants underlying the observed phenotypic variation in muscle carotenoid retention. Finally, the model framework is used to predict that a specific regulatory system controlling the release of astaxanthin from the muscle is not likely to exist, and that the release of the pigment into the blood is instead caused by the androgen-initiated autolytic degradation of the muscle in the sexually mature salmon. CONCLUSION: The results show that a dynamic model describing a complex trait can be instrumental in the early stages of a project trying to uncover underlying determinants. The model provides a heuristic basis for an experimental research programme, as well as defining a scaffold for modelling carotenoid dynamics in mammalian systems.

Understanding the sequential activation of Type III and Type VI Secretion Systems in Salmonella typhimurium using Boolean modeling.

BACKGROUND: Three pathogenicity islands, viz. SPI-1 (Salmonella pathogenicity island 1), SPI-2 (Salmonella pathogenicity island 2) and T6SS (Type VI Secretion System), present in the genome of Salmonella typhimurium have been implicated in the virulence of the pathogen. While the regulation of SPI-1 and SPI-2 (both encoding components of the Type III Secretion System - T3SS) are well understood, T6SS regulation is comparatively less studied. Interestingly, inter-connections among the regulatory elements of these three virulence determinants have also been suggested to be essential for successful infection. However, till date, an integrated view of gene regulation involving the regulators of these three secretion systems and their cross-talk is not available. RESULTS: In the current study, relevant regulatory information available from literature have been integrated into a single Boolean network, which portrays the dynamics of T3SS (SPI-1 and SPI-2) and T6SS mediated virulence. Some additional regulatory interactions involving a two-component system response regulator YfhA have also been predicted and included in the Boolean network. These predictions are aimed at deciphering the effects of osmolarity on T6SS regulation, an aspect that has been suggested in earlier studies, but the mechanism of which was hitherto unknown. Simulation of the regulatory network was able to recreate in silico the experimentally observed sequential activation of SPI-1, SPI-2 and T6SS. CONCLUSIONS: The present study integrates relevant gene regulatory data (from literature and our prediction) into a single network, representing the cross-communication between T3SS (SPI-1 and SPI-2) and T6SS. This holistic view of regulatory interactions is expected to improve the current understanding of pathogenesis of S. typhimurium.

Understanding communication signals during mycobacterial latency through predicted genome-wide protein interactions and boolean modeling.

About 90% of the people infected with Mycobacterium tuberculosis carry latent bacteria that are believed to get activated upon immune suppression. One of the fundamental challenges in the control of tuberculosis is therefore to understand molecular mechanisms involved in the onset of latency and/or reactivation. We have attempted to address this problem at the systems level by a combination of predicted functional protein:protein interactions, integration of functional interactions with large scale gene expression studies, predicted transcription regulatory network and finally simulations with a boolean model of the network. Initially a prediction for genome-wide protein functional linkages was obtained based on genome-context methods using a Support Vector Machine. This set of protein functional linkages along with gene expression data of the available models of latency was employed to identify proteins involved in mediating switch signals during dormancy. We show that genes that are up and down regulated during dormancy are not only coordinately regulated under dormancy-like conditions but also under a variety of other experimental conditions. Their synchronized regulation indicates that they form a tightly regulated gene cluster and might form a latency-regulon. Conservation of these genes across bacterial species suggests a unique evolutionary history that might be associated with M. tuberculosis dormancy. Finally, simulations with a boolean model based on the regulatory network with logical relationships derived from gene expression data reveals a bistable switch suggesting alternating latent and actively growing states. Our analysis based on the interaction network therefore reveals a potential model of M. tuberculosis latency.

Mathematical modeling of regulation of type III secretion system in Salmonella enterica serovar Typhimurium by SirA.

Salmonella enterica serovar Typhimurium invades the intestinal epithelial cells using type three secretion system (TTSS) encoded on Salmonella pathogenicity island-1 (SPI-1). The key regulator of this secretion system is HilA, which is in turn regulated by HilD, HilC and RtsA. It is also known that SirA/BarA system, a two-component regulatory system plays a crucial role in regulating HilA. There are two different mechanisms that have been proposed earlier for regulation of HilD-HilC-RtsA-HilA network by SirA. One considers SirA to be acting through HilA and HilC, whereas the other considers SirA to be acting through HilD. In this paper, we have built mathematical models corresponding to both these scenarios and carried out simulations under different gene knock-out conditions. Additionally, since the two proposed mechanisms based on the experimental data are equally likely, we also considered a mechanism which is a combination of the two proposed mechanisms. The simulations were carried out to check the levels of HilA, the factor regulating the virulence, as well as the levels of the intermediate components in the network, namely HilC and RtsA. The simulation results were used to check the consistency of various models and also to suggest the most probable mechanism of hilA regulation. The results of our study show that while most of the mathematical models are able to predict the virulence data, the models considering SirA to regulate through HilA and HilC fail to predict the levels of intermediate components, HilC and RtsA. Nevertheless, one of the models considering regulation of virulence by SirA via HilD was able to predict results comparable to the experimental data. In addition, combination of this model (regulation by SirA via HilD) with the model considering regulation by SirA through HilA and HilC, also predicted results consistent with experimental observations. Our conclusions were further validated by testing the stability of the results against changes in parameter values, thus confirming the relative robustness of the proposed modeling system.