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Gene products can affect the concentrations of small molecules (aka "metabolites"), and conversely, some metabolites can modulate the concentrations of gene transcripts. While many specific instances of this interplay have been revealed, a global approach to systematically uncover human gene-metabolite interactions is still lacking. We performed a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome using untargeted metabolome features, extracted from 1 H nuclear magnetic resonance spectroscopy (NMR) of urine samples, and gene expression levels, quantified from RNA-Seq of lymphoblastoid cell lines (LCL) from 555 healthy individuals. We identified 20 study-wide significant associations corresponding to 15 genes, of which 5 associations (with 2 genes) were confirmed with follow-up NMR data. Using metabomatching, we identified the metabolites corresponding to metabolome features associated with the genes, namely, N-acetylated compounds with ALMS1 and trimethylamine (TMA) with HPS1. Finally, Mendelian randomization analysis supported a potential causal link between the expression of genes in both the ALMS1- and HPS1-loci and their associated metabolite concentrations. In the case of HPS1, we additionally observed that TMA concentration likely exhibits a reverse causal effect on HPS1 expression levels, indicating a negative feedback loop. Our study highlights how the integration of metabolomics, gene expression, and genetic data can pinpoint causal genes modulating metabolite concentrations.

Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we test the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of co-varying features derived from Principal Component Analysis (PCA), the Iterative Signature Algorithm (ISA) and Averaged Correlation Profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. Based on the number and quality of the matches we concluded that both ISA and ACP can robustly identify about a dozen metabolites, half of which were shared, while PCA did not produce any signatures with robust matches.

International audience; Background Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. Conclusions PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and omics research domains.

As the largest extant cat species in west Asia, the leopard (Panthera pardus) shows high morphological variation, which has led to the description of seven different subspecies in the region. Different investigations have tried to clarify its phylogenetic structure; however, sample size and spatial distribution insufficiently represent the Iranian population, the largest remaining bulk of the Persian leopard (P. p. saxicolor) in the Middle East that probably functioned as a source for the subspecies' range. We examined sequence variation in the mitochondrial NADH-5 gene for 25 leopards from different parts of Iran. Also, we examined 49 adult male skulls to understand the morphological variation of the Iranian leopard population. Our craniometrical results revealed that while no differentiation is seen based on size or shape characteristics from different parts of Iran, larger individuals normally belong to the northern range. Time-calibrated Bayesian phylogenetic analysis suggested that the Iranian female lineage is a monophyletic group that diverged from a group of Asian leopards in the second half of the Pleistocene. Three closely related haplotypes were identified for the entire country: one commonly found haplotype throughout Iran, south Caucasus and Turkmenistan and two localized haplotypes were sequenced from southern Zagros and eastern Alborz. Accordingly, the Persian leopard population in Iran as well as in neighbouring countries can be protected as a large management unit through large-scale conservation planning. Moreover, the available captive stock of the Persian leopard represents an invaluable source for reintroduction for countries interested in restoring their locally extinct population. © 2015 The Linnean Society of London, Biological Journal of the Linnean Society, 2015, 114, 721–736.

The yeast cell wall is digested to allow cell fusion during sexual reproduction. How cells coordinate this process with cell–cell contact to prevent lysis is unclear. Merlini et al. show that the Ras GAP protein Gap1, which is recruited to sites of Ras-GTP, restricts Ras activity and protects cells from lysis due to premature fusion attempts., In the fission yeast Schizosaccharomyces pombe, pheromone signaling engages a signaling pathway composed of a G protein–coupled receptor, Ras, and a mitogen-activated protein kinase (MAPK) cascade that triggers sexual differentiation and gamete fusion. Cell–cell fusion requires local cell wall digestion, which relies on an initially dynamic actin fusion focus that becomes stabilized upon local enrichment of the signaling cascade on the structure. We constructed a live-reporter of active Ras1 (Ras1–guanosine triphosphate [GTP]) that shows Ras activity at polarity sites peaking on the fusion structure before fusion. Remarkably, constitutive Ras1 activation promoted fusion focus stabilization and fusion attempts irrespective of cell pairing, leading to cell lysis. Ras1 activity was restricted by the guanosine triphosphatase–activating protein Gap1, which was itself recruited to sites of Ras1-GTP and was essential to block untimely fusion attempts. We propose that negative feedback control of Ras activity restrains the MAPK signal and couples fusion with cell–cell engagement.

In fission yeast Schizosaccharomyces pombe, pheromone signalling engages a GPCR-Ras-MAPK cascade to trigger sexual differentiation leading to gamete fusion. Cell-cell fusion necessitates local cell wall digestion, the location of which relies on an initially dynamic actin fusion focus that becomes stabilized upon local enrichment of the signalling cascade. We constructed a live-reporter of active Ras1 (Ras1-GTP), also functional in S. cerevisiae, which revealed Ras activity at polarity sites peaking on the fusion structure before fusion. Remarkably, constitutive Ras1 activation promoted fusion focus stabilization and fusion attempts irrespective of cell-cell pairing, leading to cell lysis. Ras1 activity is restricted by the GTPase activating protein (GAP) Gap1, itself recruited to sites of Ras1-GTP. While the GAP domain on its own does not suffice for this localization, its recruitment to Ras1-GTP sites is essential to block untimely fusion attempts. We conclude that negative feedback control of Ras activity restrains the MAPK signal and couples fusion with cell-cell engagement.

Cell pairing is central for many processes, including immune defense, neuronal connection, hyphal fusion, and sexual reproduction. How does a cell orient toward a partner, especially when faced with multiple choices? Fission yeast Schizosaccharomyces pombe P and M cells, which respectively express P and M factor pheromones [1, 2], pair during the mating process induced by nitrogen starvation. Engagement of pheromone receptors Map3 and Mam2 [3, 4] with their cognate pheromone ligands leads to activation of the Gα protein Gpa1 to signal sexual differentiation [3, 5, 6]. Prior to cell pairing, the Cdc42 GTPase, a central regulator of cell polarization, forms dynamic zones of activity at the cell periphery at distinct locations over time [7]. Here we show that Cdc42-GTP polarization sites contain the M factor transporter Mam1, the general secretion machinery, which underlies P factor secretion, and Gpa1, suggesting that these are sub-cellular zones of pheromone secretion and signaling. Zone lifetimes scale with pheromone concentration. Computational simulations of pair formation through a fluctuating zone show that the combination of local pheromone release and sensing, short pheromone decay length, and pheromone-dependent zone stabilization leads to efficient pair formation. Consistently, pairing efficiency is reduced in the absence of the P factor protease. Similarly, zone stabilization at reduced pheromone levels, which occurs in the absence of the predicted GTPase-activating protein for Ras, leads to reduction in pairing efficiency. We propose that efficient cell pairing relies on fluctuating local signal emission and perception, which become locked into place through stimulation.