Remission of obesity and insulin resistance is not sufficient to restore mitochondrial homeostasis in visceral adipose tissue.

Metabolic plasticity is the ability of a biological system to adapt its metabolic phenotype to different environmental stressors. We used a whole-body and tissue-specific phenotypic, functional, proteomic, metabolomic and transcriptomic approach to systematically assess metabolic plasticity in diet-induced obese mice after a combined nutritional and exercise intervention. Although most obesity and overnutrition-related pathological features were successfully reverted, we observed a high degree of metabolic dysfunction in visceral white adipose tissue, characterized by abnormal mitochondrial morphology and functionality. Despite two sequential therapeutic interventions and an apparent global healthy phenotype, obesity triggered a cascade of events in visceral adipose tissue progressing from mitochondrial metabolic and proteostatic alterations to widespread cellular stress, which compromises its biosynthetic and recycling capacity. In humans, weight loss after bariatric surgery showed a transcriptional signature in visceral adipose tissue similar to our mouse model of obesity reversion. Overall, our data indicate that obesity prompts a lasting metabolic fingerprint that leads to a progressive breakdown of metabolic plasticity in visceral adipose tissue.

A Bayesian machine scientist to aid in the solution of challenging scientific problems.

Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need "machine scientists" that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods.

CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network.

MOTIVATION: The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample. RESULTS: Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools. AVAILABILITY AND IMPLEMENTATION: https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

iMet: A Network-Based Computational Tool To Assist in the Annotation of Metabolites from Tandem Mass Spectra.

Structural annotation of metabolites relies mainly on tandem mass spectrometry (MS/MS) analysis. However, approximately 90% of the known metabolites reported in metabolomic databases do not have annotated spectral data from standards. This situation has fostered the development of computational tools that predict fragmentation patterns in silico and compare these to experimental MS/MS spectra. However, because such methods require the molecular structure of the detected compound to be available for the algorithm, the identification of novel metabolites in organisms relevant for biotechnological and medical applications remains a challenge. Here, we present iMet, a computational tool that facilitates structural annotation of metabolites not described in databases. iMet uses MS/MS spectra and the exact mass of an unknown metabolite to identify metabolites in a reference database that are structurally similar to the unknown metabolite. The algorithm also suggests the chemical transformation that converts the known metabolites into the unknown one. As a proxy for the structural annotation of novel metabolites, we tested 148 metabolites following a leave-one-out cross-validation procedure or by using MS/MS spectra experimentally obtained in our laboratory. We show that for 89% of the 148 metabolites at least one of the top four matches identified by iMet enables the proper annotation of the unknown metabolites. To further validate iMet, we tested 31 metabolites proposed in the 2012-16 CASMI challenges. iMet is freely available at http://imet.seeslab.net .

A comprehensive study on different modelling approaches to predict platelet deposition rates in a perfusion chamber.

Thrombus formation is a multiscale phenomenon triggered by platelet deposition over a protrombotic surface (eg. a ruptured atherosclerotic plaque). Despite the medical urgency for computational tools that aid in the early diagnosis of thrombotic events, the integration of computational models of thrombus formation at different scales requires a comprehensive understanding of the role and limitation of each modelling approach. We propose three different modelling approaches to predict platelet deposition. Specifically, we consider measurements of platelet deposition under blood flow conditions in a perfusion chamber for different time periods (3, 5, 10, 20 and 30 minutes) at shear rates of 212 s(-1), 1390 s(-1) and 1690 s(-1). Our modelling approaches are: i) a model based on the mass-transfer boundary layer theory; ii) a machine-learning approach; and iii) a phenomenological model. The results indicate that the three approaches on average have median errors of 21%, 20.7% and 14.2%, respectively. Our study demonstrates the feasibility of using an empirical data set as a proxy for a real-patient scenario in which practitioners have accumulated data on a given number of patients and want to obtain a diagnosis for a new patient about whom they only have the current observation of a certain number of variables.

Implementation of an algorithm based on the Runge-Kutta-Fehlberg technique and the potential energy as a reaction coordinate to locate intrinsic reaction paths.

The intrinsic reaction coordinate (IRC) curve is used widely as a representation of the Reaction Path and can be parameterized taking the potential energy as a reaction coordinate (Aguilar-Mogas et al., J Chem Phys 2008, 128, 104102). Taking this parameterization and its variational nature, an algorithm is proposed that permits to locate this type of curve joining two points from an arbitrary curve that joints the same initial and final points. The initial and final points are minima of the potential energy surface associated with the geometry of reactants and products of the reaction whose mechanism is under study. The arbitrary curves are moved toward the IRC curve by a Runge-Kutta-Fehlberg technique. This technique integrates a set of differential equations resulting from the minimization until value zero of the line integral over the Weierstrass E-function. The Weierstrass E-function is related with the second variation in the theory of calculus of variations. The algorithm has been proved in real chemical systems.

Finding reaction paths using the potential energy as reaction coordinate.

The intrinsic reaction coordinate curve (IRC), normally proposed as a representation of a reaction path, is parametrized as a function of the potential energy rather than the arc-length. This change in the parametrization of the curve implies that the values of the energy of the potential energy surface points, where the IRC curve is located, play the role of reaction coordinate. We use Caratheodory's relation to derive in a rigorous manner the proposed parametrization of the IRC path. Since this Caratheodory's relation is the basis of the theory of calculus of variations, then this fact permits to reformulate the IRC model from this mathematical theory. In this mathematical theory, the character of the variational solution (either maximum or minimum) is given through the Weierstrass E-function. As proposed by Crehuet and Bofill [J. Chem. Phys. 122, 234105 (2005)], we use the minimization of the Weierstrass E-function, as a function of the potential energy, to locate an IRC path between two minima from an arbitrary curve on the potential energy surface, and then join these two minima. We also prove, from the analysis of the Weierstrass E-function, the mathematical bases for the algorithms proposed to locate the IRC path. The proposed algorithm is applied to a set of examples. Finally, the algorithm is used to locate a discontinuous, or broken, IRC path, namely, when the path connects two first order saddle points through a valley-ridged inflection point.