Javier González

Protein biogenesis machinery is a driver of replicative aging in yeast

An integrated account of the molecular changes occurring during the process of cellular aging is crucial towards understanding the underlying mechanisms. Here, using novel culturing and computational methods as well as latest analytical techniques, we mapped the proteome and transcriptome during the replicative lifespan of budding yeast. With age, we found primarily proteins involved in protein biogenesis to increase relative to their transcript levels. Exploiting the dynamic nature of our data, we reconstructed high-level directional networks, where we found the same protein biogenesis-related genes to have the strongest ability to predict the behavior of other genes in the system. We identified metabolic shifts and the loss of stoichiometry in protein complexes as being consequences of aging. We propose a model whereby the uncoupling of protein levels of biogenesis-related genes from their transcript levels is causal for the changes occurring in aging yeast. Our model explains why targeting protein synthesis, or repairing the downstream consequences, can serve as interventions in aging. DOI: http://dx.doi.org/10.7554/eLife.08527.001

Cross-class metallo-β-lactamase inhibition by bisthiazolidines reveals multiple binding modes

Significance Bacterial diseases remain a huge burden on healthcare worldwide, with the emergence and re-emergence of strains resistant to currently used antibiotics posing an increasing clinical threat. Metallo-β-lactamases (MBLs) are key determinants of antibiotic resistance because they hydrolyze almost all β-lactam antibiotics and are unaffected by currently available β-lactamase inhibitors (βLIs). The structural diversity between MBLs has proved problematic when designing βLIs effective against all MBL targets. Here we show a series of small compounds, bisthiazolidines, which act as inhibitors of all MBL types, restoring the efficacy of currently used antibiotics against resistant bacterial strains producing different MBLs. High-resolution crystal structures reveal how diverse MBLs are inhibited by the unexpected versatility of bisthiazolidine binding, raising implications for future βLI design. Metallo-β-lactamases (MBLs) hydrolyze almost all β-lactam antibiotics and are unaffected by clinically available β-lactamase inhibitors (βLIs). Active-site architecture divides MBLs into three classes (B1, B2, and B3), complicating development of βLIs effective against all enzymes. Bisthiazolidines (BTZs) are carboxylate-containing, bicyclic compounds, considered as penicillin analogs with an additional free thiol. Here, we show both l- and d-BTZ enantiomers are micromolar competitive βLIs of all MBL classes in vitro, with Kis of 6–15 µM or 36–84 µM for subclass B1 MBLs (IMP-1 and BcII, respectively), and 10–12 µM for the B3 enzyme L1. Against the B2 MBL Sfh-I, the l-BTZ enantiomers exhibit 100-fold lower Kis (0.26–0.36 µM) than d-BTZs (26–29 µM). Importantly, cell-based time-kill assays show BTZs restore β-lactam susceptibility of Escherichia coli-producing MBLs (IMP-1, Sfh-1, BcII, and GOB-18) and, significantly, an extensively drug-resistant Stenotrophomonas maltophilia clinical isolate expressing L1. BTZs therefore inhibit the full range of MBLs and potentiate β-lactam activity against producer pathogens. X-ray crystal structures reveal insights into diverse BTZ binding modes, varying with orientation of the carboxylate and thiol moieties. BTZs bind the di-zinc centers of B1 (IMP-1; BcII) and B3 (L1) MBLs via the free thiol, but orient differently depending upon stereochemistry. In contrast, the l-BTZ carboxylate dominates interactions with the monozinc B2 MBL Sfh-I, with the thiol uninvolved. d-BTZ complexes most closely resemble β-lactam binding to B1 MBLs, but feature an unprecedented disruption of the D120–zinc interaction. Cross-class MBL inhibition therefore arises from the unexpected versatility of BTZ binding.

Ni(II) coordination to mixed sites modulates DNA binding of HpNikR via a long-range effect

Helicobacter pylori NikR (HpNikR) is a nickel-dependent transcription factor that regulates multiple genes in the H. pylori pathogen. There are conflicting data regarding the locations of the Ni(II) sites and the role of Ni(II) coordination in DNA recognition. Herein, we report crystal structures of (i) the metal-binding domain (MBD) of HpNikR (3.08 Å) and (ii) a mutant, H74A (2.04 Å), designed to disrupt native Ni(II) coordination. In the MBD structure, four nickel ions are coordinated to two different types of nickel sites (4-coordinate, square planar, and 5/6-coordinate, square pyramidal/octahedral). In the H74A structure, all four nickel ions are coordinated to 4-coordinate square-planar sites. DNA-binding studies reveal tighter binding for target DNA sequences for holo-HpNikR compared with the affinities of Ni(II) reconstituted apo-HpNikR and H74A for these same DNA targets, supporting a role for Ni(II) coordination to 5/6 sites in DNA recognition. Small-angle X-ray scattering studies of holo-HpNikR and H74A reveal a high degree of conformational flexibility centered at the DNA-binding domains of H74A, which is consistent with disorder observed in the crystal structure of the protein. A model of DNA recognition by HpNikR is proposed in which Ni(II) coordination to specific sites in the MBD have a long-range effect on the flexibility of the DNA-binding domains and, consequently, the DNA recognition properties.

Construction and use of a microfluidic dissection platform for long-term imaging of cellular processes in budding yeast

This protocol describes the production and operation of a microfluidic dissection platform for long-term, high-resolution imaging of budding yeast cells. At the core of this platform is an array of micropads that trap yeast cells in a single focal plane. Newly formed daughter cells are subsequently washed away by a continuous flow of fresh culture medium. In a typical experiment, 50–100 cells can be tracked during their entire replicative lifespan. Apart from aging-related research, the microfluidic platform can also be a valuable tool for other studies requiring the monitoring of single cells over time. Here we provide step-by-step instructions on how to fabricate the silicon wafer mold, how to produce and operate the microfluidic device and how to analyze the obtained data. Production of the microfluidic dissection platform and setting up an aging experiment takes ∼7 h.

Representing functional data using support vector machines

Functional data are difficult to manage for most classical statistical techniques, given the very high (or intrinsically infinite) dimensionality. The reason lies in that functional data are functions and most algorithms are designed to work with low dimensional vectors. In this paper we propose a functional analysis technique to obtain finite-dimensional representations of functional data. The key idea is to consider each functional datum as a point in a general function space and then to project these points onto a Reproducing Kernel Hilbert Space with the aid of a support vector machine. We show some theoretical properties of the method and illustrate its performance in some classification examples.

Calorie restriction does not elicit a robust extension of replicative lifespan in Saccharomyces cerevisiae

Significance Calorie restriction (CR) has been shown to extend the lifespans of various organisms. Consequently, a considerable amount of research has been performed to elucidate its mechanisms, especially in the yeast Saccharomyces cerevisiae. Here, we show that due to small sample sizes, large variation exists between measurements. In addition, the effect of CR on lifespan has been routinely overestimated in yeast due to the use of short-lived experimental controls, which together may explain why contradictory mechanisms were found to mediate CR-induced lifespan extension. Moreover, we did not observe any lifespan-enhancing effect of CR using an alternative measurement technique. The inability of CR to robustly extend lifespan suggests that calories alone do not modulate the lifespan of this important model organism. Calorie restriction (CR) is often described as the most robust manner to extend lifespan in a large variety of organisms. Hence, considerable research effort is directed toward understanding the mechanisms underlying CR, especially in the yeast Saccharomyces cerevisiae. However, the effect of CR on lifespan has never been systematically reviewed in this organism. Here, we performed a meta-analysis of replicative lifespan (RLS) data published in more than 40 different papers. Our analysis revealed that there is significant variation in the reported RLS data, which appears to be mainly due to the low number of cells analyzed per experiment. Furthermore, we found that the RLS measured at 2% (wt/vol) glucose in CR experiments is partly biased toward shorter lifespans compared with identical lifespan measurements from other studies. Excluding the 2% (wt/vol) glucose experiments from CR experiments, we determined that the average RLS of the yeast strains BY4741 and BY4742 is 25.9 buds at 2% (wt/vol) glucose and 30.2 buds under CR conditions. RLS measurements with a microfluidic dissection platform produced identical RLS data at 2% (wt/vol) glucose. However, CR conditions did not induce lifespan extension. As we excluded obvious methodological differences, such as temperature and medium, as causes, we conclude that subtle method-specific factors are crucial to induce lifespan extension under CR conditions in S. cerevisiae.

Inferring latent gene regulatory network kinetics

Abstract Regulatory networks consist of genes encoding transcription factors (TFs) and the genes they activate or repress. Various types of systems of ordinary differential equations (ODE) have been proposed to model these networks, ranging from linear to Michaelis-Menten approaches. In practice, a serious drawback to estimate these models is that the TFs are generally unobserved. The reason is the actual lack of high-throughput techniques to measure abundance of proteins in the cell. The challenge is to infer their activity profile together with the kinetic parameters of the ODE using level expression measurements of the genes they regulate. In this work we propose general statistical framework to infer the kinetic parameters of regulatory networks with one or more TFs using time course gene expression data. Our approach is also able to predict the activity levels of the TF. We use a penalized likelihood approach where the ODE is used as a penalty. The main advantage is that the solution of the ODE is not required explicitly as it is common in most proposed methods. This makes our approach computationally efficient and suitable for large systems with many components. We use the proposed method to study a SOS repair system in Escherichia coli. The reconstructed TF exhibits a similar behavior to experimentally measured profiles and the genetic expression data are fitted properly.

Reproducing kernel Hilbert space based estimation of systems of ordinary differential equations

Abstract Non-linear systems of differential equations have attracted the interest in fields like system biology, ecology or biochemistry, due to their flexibility and their ability to describe dynamical systems. Despite the importance of such models in many branches of science they have not been the focus of systematic statistical analysis until recently. In this work we propose a general approach to estimate the parameters of systems of differential equations measured with noise. Our methodology is based on the maximization of the penalized likelihood where the system of differential equations is used as a penalty. To do so, we use a reproducing kernel Hilbert space approach that allows us to formulate the estimation problem as an unconstrained numeric maximization problem easy to solve. The proposed method is tested with synthetically simulated data and it is used to estimate the unobserved transcription factor cdaR in Streptomyces coelicolor using gene expression data of the genes it regulates.

Time-course window estimator for ordinary differential equations linear in the parameters

In many applications obtaining ordinary differential equation descriptions of dynamic processes is scientifically important. In both, Bayesian and likelihood approaches for estimating parameters of ordinary differential equations, the speed and the convergence of the estimation procedure may crucially depend on the choice of initial values of the parameters. Extending previous work, we show in this paper how using window smoothing yields a fast estimator for systems that are linear in the parameters. Using weak assumptions on the measurement error, we prove that the proposed estimator is