Monomorphism of human cytochrome c

Cytochrome c (Cyt c) has key roles in both mitochondrial electron transfer and apoptosis onset and is therefore likely undergoing a strong selective pressure against amino acid variation. Nevertheless, a phylogenetically fast amino acid replacement rate in the Cyt c of species of the anthropoid primate lineage was recently reported. We therefore looked for the presence of nonsynonymous single nucleotide polymorphisms (nsSNPs) in the human Cyt c (HGNC approved gene symbol: CYCS), which, given its cellular constraints, could have important functional consequences, and found a large number of putative nsSNPs reported in the dbSNP database. We then subjected these putative SNPs to experimental validation by sequencing the Cyt c gene in a panel of 95 individuals assumed as a standard reference of the human population diversity. Surprisingly, none of the putative SNPs survived experimental validation. We conclude that non-rare allelic variants of the Cyt c protein are absent in the human populations analyzed in this study.     

Group-wise ANOVA simultaneous component analysis for designed <Emphasis Type="Italic">omics</Emphasis> experiments

Introduction

Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation.

Objectives

We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret.

Methods

GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices.

Results

The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes.

Conclusions

GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.

     

From correlation to causation: analysis of metabolomics data using systems biology approaches

Introduction

Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

Objectives

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

Methods

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

Results

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

Conclusions

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

     

The Silk Road, Marco Polo, a Bible and its proteome: a detective story

Around the end of XIII century (at the time of young Marco Polo's first trip to China at the court of Khubilai Khan in Khan Baliq) a pocket Bible was delivered by a Franciscan friar to the Mogul Emperor, in the framework of the evangelization program of the Far East. Four centuries later, in 1685, this Bible was rediscovered by the Jesuit Philippe Couplet in the house of a rich Chinese in Nanchin and donated to Cosimo III, Grand Duke of Tuscany. This Bible was recently "unearthed" in the Biblioteca Medicea Laurenziana in Florence, wrapped up in a precious yellow silk cloth, in a rather ruined state. After two years of restoration, the Bible will return to China in 2012 for a celebration of its >700years of life and of its remarkable return trip on the Silk Road. On account of the thinness of the parchment (barely 80μm thickness, the size of each foil being 16.5×11cm) it was widely held that the pages were produced from foetal lambskins. On tiny fragments of the margins of a foil, after several unsuccessful attempts at digesting the vellum, we were able to obtain a tryptic peptide mixture, which, upon mass spectrometry analysis, yielded the identity of 8 unique proteins, belonging to the genus Bos taurus, thus confirming the origin of the vellum from calfskins rather than from foetal lambskins. Our results prove that it is possible to obtain reliable protein extraction and IDs from ancient parchment documents.     

The war of tools: how can NMR spectroscopists detect errors in their structures?

Protein structure determination by NMR methods has started in the mid-eighties and has been growing steadily since then. Ca. 14% of the protein structures deposited in the PDB have been solved by NMR. The evaluation of the quality of NMR structures however is still lacking a well-established practice. In this work, we examined various tools for the assessment of structural quality to ascertain the extent to which these tools could be applied to detect flaws in NMR structures. In particular, we investigated the variation in the scores assigned by these programs as a function of the deviation of the structures induced by errors in assignments or in the upper distance limits used. These perturbations did not distort radically the protein fold, but resulted in backbone RMS deviations up to 3 Å, which is in line with errors highlighted in the available literature. We found that it is quite difficult to discriminate the structures perturbed because of misassignments from the original ones, also because the spread in score over the conformers of the original bundle is relatively large. ?–ψ distributions and normality scores related to the backbone conformation and to the distribution of side-chain dihedral angles are the most sensitive indicators of flaws.     

Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies

Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q ² and Discriminant Q ² (DQ ²) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q ² and Discriminant Q ² (DQ ²). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ ² and Q ² (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies.

     

Simplivariate models: uncovering the underlying biology in functional genomics data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.     

Assessing the Metabolic Diversity of Streptococcus from a Protein Domain Point of View

Understanding the diversity and robustness of the metabolism of bacteria is fundamental for understanding how bacteria evolve and adapt to different environments. In this study, we characterised 121 Streptococcus strains and studied metabolic diversity from a protein domain perspective. Metabolic pathways were described in terms of the promiscuity of domains participating in metabolic pathways that were inferred to be functional. Promiscuity was defined by adapting existing measures based on domain abundance and versatility. The approach proved to be successful in capturing bacterial metabolic flexibility and species diversity, indicating that it can be described in terms of reuse and sharing functional domains in different proteins involved in metabolic activity. Additionally, we showed striking differences among metabolic organisation of the pathogenic serotype 2 Streptococcus suis and other strains.     

Reflections on univariate and multivariate analysis of metabolomics data

Metabolomics experiments usually result in a large quantity of data. Univariate and multivariate analysis techniques are routinely used to extract relevant information from the data with the aim of providing biological knowledge on the problem studied. Despite the fact that statistical tools like the t test, analysis of variance, principal component analysis, and partial least squares discriminant analysis constitute the backbone of the statistical part of the vast majority of metabolomics papers, it seems that many basic but rather fundamental questions are still often asked, like: Why do the results of univariate and multivariate analyses differ? Why apply univariate methods if you have already applied a multivariate method? Why if I do not see something univariately I see something multivariately? In the present paper we address some aspects of univariate and multivariate analysis, with the scope of clarifying in simple terms the main differences between the two approaches. Applications of the t test, analysis of variance, principal component analysis and partial least squares discriminant analysis will be shown on both real and simulated metabolomics data examples to provide an overview on fundamental aspects of univariate and multivariate methods.     

Microbial Community Response of an Organohalide Respiring Enrichment Culture to Permanganate Oxidation

While in situ chemical oxidation is often used to remediate tetrachloroethene (PCE) contaminated locations, very little is known about its influence on microbial composition and organohalide respiration (OHR) activity. Here, we investigate the impact of oxidation with permanganate on OHR rates, the abundance of organohalide respiring bacteria (OHRB) and reductive dehalogenase (rdh) genes using quantitative PCR, and microbial community composition through sequencing of 16S rRNA genes. A PCE degrading enrichment was repeatedly treated with low (25 μmol), medium (50 μmol), or high (100 μmol) permanganate doses, or no oxidant treatment (biotic control). Low and medium treatments led to higher OHR rates and enrichment of several OHRB and rdh genes, as compared to the biotic control. Improved degradation rates can be attributed to enrichment of (1) OHRB able to also utilize Mn oxides as a terminal electron acceptor and (2) non-dechlorinating community members of the Clostridiales and Deltaproteobacteria possibly supporting OHRB by providing essential co-factors. In contrast, high permanganate treatment disrupted dechlorination beyond cis-dichloroethene and caused at least a 2–4 orders of magnitude reduction in the abundance of all measured OHRB and rdh genes, as compared to the biotic control. High permanganate treatments resulted in a notably divergent microbial community, with increased abundances of organisms affiliated with Campylobacterales and Oceanospirillales capable of dissimilatory Mn reduction, and decreased abundance of presumed supporters of OHRB. Although OTUs classified within the OHR-supportive order Clostridiales and OHRB increased in abundance over the course of 213 days following the final 100 μmol permanganate treatment, only limited regeneration of PCE dechlorination was observed in one of three microcosms, suggesting strong chemical oxidation treatments can irreversibly disrupt OHR. Overall, this detailed investigation into dose-dependent changes of microbial composition and activity due to permanganate treatment provides insight into the mechanisms of OHR stimulation or disruption upon chemical oxidation.