Reductive genome evolution from the mother of Rickettsia

The Rickettsia genus is a group of obligate intracellular α-proteobacteria representing a paradigm of reductive evolution. Here, we investigate the evolutionary processes that shaped the genomes of the genus. The reconstruction of ancestral genomes indicates that their last common ancestor contained more genes, but already possessed most traits associated with cellular parasitism. The differences in gene repertoires across modern Rickettsia are mainly the result of differential gene losses from the ancestor. We demonstrate using computer simulation that the propensity of loss was variable across genes during this process. We also analyzed the ratio of nonsynonymous to synonymous changes (Ka/Ks) calculated as an average over large sets of genes to assay the strength of selection acting on the genomes of Rickettsia, Anaplasmataceae, and free-living γ-proteobacteria. As a general trend, Ka/Ks were found to decrease with increasing divergence between genomes. The high Ka/Ks for closely related genomes are probably due to a lag in the removal of slightly deleterious nonsynonymous mutations by natural selection. Interestingly, we also observed a decrease of the rate of gene loss with increasing divergence, suggesting a similar lag in the removal of slightly deleterious pseudogene alleles. For larger divergence (Ks > 0.2), Ka/Ks converge toward similar values indicating that the levels of selection are roughly equivalent between intracellular α-proteobacteria and their free-living relatives. This contrasts with the view that obligate endocellular microorganisms tend to evolve faster as a consequence of reduced effectiveness of selection, and suggests a major role of enhanced background mutation rates on the fast protein divergence in the obligate intracellular α-proteobacteria.     

Genomic correlates of hyperthermostability,an update

It has been shown (Cambillau, C., and Claverie, J. M. (2000) J. Biol. Chem. 275, 32383-32386) that a large difference between the proportions of charged versus polar (non-charged) amino acids (CvP-bias) was an adequate, if empirical, signature of the proteome of hyperthermophilic organisms (T(growth) >80 degrees C). Since that study, the number of available microbial genomes has more than doubled, raising the possibility that the simple CvP-bias rule might no longer hold. Taking advantage of the new sequence data, we re-analyzed the genomes of 9 fully sequenced thermophiles, 9 hyperthermophiles, and 53 mesothermophile microorganisms to identify the genomic correlates of hyperthermostability on a wider data set. Our new results confirm that the CvP-bias previously identified on a much smaller data set still holds. Moreover, we show that it is an optimal criterion, in the sense that it corresponds to the most discriminating factor between hyperthermophilic and mesothermophilic microorganisms in a principal component analysis. In parallel, we evaluated two other recently proposed correlates of hyperthermostability, the proteome average pI and the dinucleotide statistical index (Kawashima, T., Amano, N., Koike, H., Makino, S., Higuchi, S., Kawashima-Ohya, Y., Watanabe, K., Yamazaki, M., Kanehori, K., Kawamoto, T., Nunoshiba, T., Yamamoto, Y., Aramaki, H., Makino, K., and Suzuki, M. (2000) Proc. Natl. Acad. Sci. 97, 14257-14262). We show that the CvP-bias is the sole criterion that is able to clearly discriminate hyperthermophile from mesothermophile microorganisms on a global genomic basis.     

Determination of strongly overlapping signaling activity from microarray data

Background

The Tissue Microarray (TMA) facilitates high-throughput analysis of hundreds of tissue specimens simultaneously. However, bottlenecks in the storage and manipulation of the data generated from TMA reviews have become apparent. A number of software applications have been developed to assist in image and data management; however no solution currently facilitates the easy online review, scoring and subsequent storage of images and data associated with TMA experimentation.

Results

This paper describes the design, development and validation of the Virtual Tissue Matrix (VTM). Through an intuitive HTML driven user interface, the VTM provides digital/virtual slide based images of each TMA core and a means to record observations on each TMA spot. Data generated from a TMA review is stored in an associated relational database, which facilitates the use of flexible scoring forms. The system allows multiple users to record their interpretation of each TMA spot for any parameters assessed. Images generated for the VTM were captured using a standard background lighting intensity and corrective algorithms were applied to each image to eliminate any background lighting hue inconsistencies or vignetting. Validation of the VTM involved examination of inter-and intra-observer variability between microscope and digital TMA reviews. Six bladder TMAs were immunohistochemically stained for E-Cadherin, β-Catenin and PhosphoMet and were assessed by two reviewers for the amount of core and tumour present, the amount and intensity of membrane, cytoplasmic and nuclear staining.

Conclusion

Results show that digital VTM images are representative of the original tissue viewed with a microscope. There were equivalent levels of inter-and intra-observer agreement for five out of the eight parameters assessed. Results also suggest that digital reviews may correct potential problems experienced when reviewing TMAs using a microscope, for example, removal of background lighting variance and tint, and potential disorientation of the reviewer, which may have resulted in the discrepancies evident in the remaining three parameters.     

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

Background

Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.

Methods

We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n?=?1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci.

Results

Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.

Conclusion

Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.

     

Identification of putative biomarkers for type 2 diabetes using metabolomics in the Korea Association REsource (KARE) cohort

Introduction

Type 2 diabetes (T2D) is a multifactorial disease resulting from a complex interaction between environmental and genetic risk factors. Metabolomics provide a logical framework that reflects the functional endpoints of biological processes being triggered by genetic information and various external influences.

Objectives

Identification of metabolite biomarkers can shed insight into etiological pathways and improve the prediction of disease risk. Here, we aimed to identify serum metabolites as putative biomarkers for T2D and their association with genetic variants in the Korean population.

Methods

A targeted metabolomics approach was employed to quantify serum metabolites for 2240 participants in the Korea Association REsource (KARE) cohort. T2D-related metabolites were identified by statistical methods including multivariable linear and logistic regression, and were independently replicated in the Cooperative Health Research in the Region of Augsburg (KORA) cohort. Additionally, by combining a genome wide association study (GWAS) with metabolomics, genetic variants associated with the identified T2D-related metabolites were uncovered.

Results

123 metabolites were quantified from fasting serum samples and four metabolites, hexadecanoylcarnitine (C16), glycine, lysophosphatidylcholine acyl C18:2 (lysoPC a C18:2), and phosphatidylcholine acyl-alkyl C36:0 (PC ae C36:0), were significantly altered in T2D compared to non-T2D subjects (after the Bonferroni correction for multiple testing with P < 4.07E ? 04, α = 0.05). Among them, C16, glycine, and lysoPC a C18:2 were independently replicated in the KORA cohort. Alterations of these metabolites were associated with ten genetic loci including six that were previously implicated in T2D or obesity.

Conclusion

Using a targeted-metabolomics and in combination with GWAS approach, we identified three serum metabolites associated with risk of T2D in both the KARE and KORA cohort and discovered ten genetic variants in relation to the identified metabolites. These findings provide a better understanding to develop novel preventive strategies for T2D in the Korean population.

     

The phosphatase Ppt1 is a dedicated regulator of the molecular chaperone Hsp90

Ppt1 is the yeast member of a novel family of protein phosphatases, which is characterized by the presence of a tetratricopeptide repeat (TPR) domain. Ppt1 is known to bind to Hsp90, a molecular chaperone that performs essential functions in the folding and activation of a large number of client proteins. The function of Ppt1 in the Hsp90 chaperone cycle remained unknown. Here, we analyzed the function of Ppt1 in vivo and in vitro. We show that purified Ppt1 specifically dephosphorylates Hsp90. This activity requires Hsp90 to be directly attached to Ppt1 via its TPR domain. Deletion of the ppt1 gene leads to hyperphosphorylation of Hsp90 in vivo and an apparent decrease in the efficiency of the Hsp90 chaperone system. Interestingly, several Hsp90 client proteins were affected in a distinct manner. Our findings indicate that the Hsp90 multichaperone cycle is more complex than was previously thought. Besides its regulation via the Hsp90 ATPase activity and the sequential binding and release of cochaperones, with Ppt1, a specific phosphatase exists, which positively modulates the maturation of Hsp90 client proteins.     

Structural genomics of highly conserved microbial genes of unknown function in search of new antibacterial targets

With more than 100 antibacterial drugs at our disposal in the 1980s, the problem of bacterial infection was considered solved. Today, however, most hospital infections are insensitive to several classes of antibacterial drugs, and deadly strains of Staphylococcus aureus resistant to vancomycin – the last resort antibiotic – have recently begin to appear. Other life-threatening microbes, such as Enterococcus faecalis and Mycobacterium tuberculosis are already able to resist every available antibiotic. There is thus an urgent, and continuous need for new, preferably large-spectrum, antibacterial molecules, ideally targeting new biochemical pathways. Here we report on the progress of our structural genomics program aiming at the discovery of new antibacterial gene targets among evolutionary conserved genes of uncharacterized function. A series of bioinformatic and comparative genomics analyses were used to identify a set of 221 candidate genes common to Gram-positive and Gram-negative bacteria. These genes were split between two laboratories. They are now submitted to a systematic 3-D structure determination protocol including cloning, protein expression and purification, crystallization, X-ray diffraction, structure interpretation, and function prediction. We describe here our strategies for the 111 genes processed in our laboratory. Bioinformatics is used at most stages of the production process and out of 111 genes processed – and 17 months into the project – 108 have been successfully cloned, 103 have exhibited detectable expression, 84 have led to the production of soluble protein, 46 have been purified, 12 have led to usable crystals, and 7 structures have been determined.