On the effects of alternative optima in context-specific metabolic model predictions

Recent methodological developments have facilitated the integration of high-throughput data into genome-scale models to obtain context-specific metabolic reconstructions. A unique solution to this data integration problem often may not be guaranteed, leading to a multitude of context-specific predictions equally concordant with the integrated data. Yet, little attention has been paid to the alternative optima resulting from the integration of context-specific data. Here we present computational approaches to analyze alternative optima for different context-specific data integration instances. By using these approaches on metabolic reconstructions for the leaf of Arabidopsis thaliana and the human liver, we show that the analysis of alternative optima is key to adequately evaluating the specificity of the predictions in particular cellular contexts. While we provide several ways to reduce the ambiguity in the context-specific predictions, our findings indicate that the existence of alternative optimal solutions warrant caution in detailed context-specific analyses of metabolism

Geo-Mars: Aprendiendo geología utilizando datos de las misiones espaciales a Marte

En este trabajo presento una propuesta de innovación docente para la enseñanzadecontenidos geológicos dentro de la asignatura de Biología y Geología de 1º de Bachillerato. Específicamente, propongo emplear algunos aspectos de la geología marciana comorecursodidáctico para la enseñanza de contenidos geológicos. Para ello, desarrollo una actividaddetipo investigación cooperativa, en la que el alumnado participa en el diseño de unamisiónficticia al planeta Marte para buscar indicios de vida pasada. Durante la actividad, el alumnado aplicará conocimientos de un criterio de evaluación del currículo oficial al proponer un cráter de impacto candidato como destino de la misión. He diseñadoestapropuesta guiándome por los principios del aprendizaje significativo, aprendizajepordescubrimiento, aprendizaje cooperativo y la gamificaciónIn this work, I present a proposal for the innovative teaching of geological contents withinthe subject of Biology and Geology of eleventh grade (1º de Bachillerato in theSpanisheducation system). Specifically, I propose to employ some aspects of Martian geologyasaninnovative strategy for the teaching of geological contents. To this end, I developacooperative-investigative type of activity, in which students participate in the designof afictitious mission to planet Mars aimed at finding signs of past life. During theactivity, students will apply content from the official curriculum to propose a candidate impact crateras a destination for the mission. I have designed this proposal guided by the principlesof meaningful learning, learning by discovery, cooperative learning, and gamification

A ubiquitous gammaproteobacterial clade dominates expression of sulfur oxidation genes across the mesopelagic ocean

21 pages, 6 figures, supplementary information https://doi.org/10.1038/s41564-023-01374-2.-- Data availability: The sequence data generated in this study have been deposited in the EMBL Nucleotide Sequence Database (ENA) database under Bioproject PRJEB35712 (metagenomic and metatranscriptomic raw reads, metagenomic and metatranscriptomic assemblies, metagenomic assembled genomes and single-cell amplified genomes) and in the NCBI Sequence Read Archive (SRA) under Bioproject PRJNA593264 (16S rRNA amplicon reads).-- Code availability: Scripts available at Zenodo (https://doi.org/10.5281/zenodo.7721930.2023)The deep ocean (>200 m depth) is the largest habitat on Earth. Recent evidence suggests sulfur oxidation could be a major energy source for deep ocean microbes. However, the global relevance and the identity of the major players in sulfur oxidation in the oxygenated deep-water column remain elusive. Here we combined single-cell genomics, community metagenomics, metatranscriptomics and single-cell activity measurements on samples collected beneath the Ross Ice Shelf in Antarctica to characterize a ubiquitous mixotrophic bacterial group (UBA868) that dominates expression of RuBisCO genes and of key sulfur oxidation genes. Further analyses of the gene libraries from the ‘Tara Oceans’ and ‘Malaspina’ expeditions confirmed the ubiquitous distribution and global relevance of this enigmatic group in the expression of sulfur oxidation and dissolved inorganic carbon fixation genes across the global mesopelagic ocean. Our study also underscores the unrecognized importance of mixotrophic microbes in the biogeochemical cycles of the deep oceanThis research was facilitated by the New Zealand Antarctic Research Institute (NZARI)-funded Aotearoa New Zealand Ross Ice Shelf Program. Samples for MICRO-CARD-FISH were collected on several research cruises led by M. Simon (Sonne 248 cruise), B. Quéguiner and I. Obernosterer (MobyDick) and L. J. A. Gerringa (Geotraces-1). F.B. was supported by the Austrian Science Fund (FWF) projects OCEANIDES (P34304-B), ENIGMA (TAI534), EXEBIO (P35248) and OCEANBIOPLAST (P35619-B). G.J.H. was supported by the FWF project ARTEMIS (P28781-B21) and I486-B09 and by the ERC under the European Community’s 7th Framework Programme (FP7/2007-2013)/ERC grant agreement no. 268595 (MEDEA project). R.L. was supported by INTERACTOMICS, CTM2015-69936-P, and J.M.G. by project PID2019-110011RB-C32 (Spanish Ministry of Science and Innovation, Spanish State Research Agency, doi: 10.13039/501100011033)With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewe

Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization.

Genome-scale metabolic models have proven highly valuable in investigating cell physiology. Recent advances include the development of methods to extract context-specific models capable of describing metabolism under more specific scenarios (e.g., cell types). Yet, none of the existing computational approaches allows for a fully automated model extraction and determination of a flux distribution independent of user-defined parameters. Here we present RegrEx, a fully automated approach that relies solely on context-specific data and ℓ1-norm regularization to extract a context-specific model and to provide a flux distribution that maximizes its correlation to data. Moreover, the publically available implementation of RegrEx was used to extract 11 context-specific human models using publicly available RNAseq expression profiles, Recon1 and also Recon2, the most recent human metabolic model. The comparison of the performance of RegrEx and its contending alternatives demonstrates that the proposed method extracts models for which both the structure, i.e., reactions included, and the flux distributions are in concordance with the employed data. These findings are supported by validation and comparison of method performance on additional data not used in context-specific model extraction. Therefore, our study sets the ground for applications of other regularization techniques in large-scale metabolic modeling

Summary of the alternative optima space of the evaluated network-centered methods.

<p>Summary of the alternative optima space of the evaluated network-centered methods.</p

A depiction of the alternative optima space of a toy RegrEx data integration problem.

<p>(A) A toy data integration problem for a metabolic network with three reactions, <i>v</i>_<i>1-3</i>, and two reaction-associated data values, <i>d</i>_<i>1-2</i> is presented. In RegrEx, the optimization problem consists of finding a flux distribution, <i>v</i>_<i>opt</i>, which minimizes the distance to the data being integrated and is compatible with the mass balance and thermodynamic constraints. In this example, only two of the three reactions are data-bounded; thus, the third, <i>v</i>_<i>3</i>, is free to vary its flux value without affecting the minimum overall distance in (B). This situation is depicted in (C), where the flux cone (the set of flux distributions, <i>v</i>, that are compatible with the imposed constraints) is projected onto the two-dimensional space where the data vector, d, resides, and the search for the optimal, <i>v</i>_<i>opt</i> is conducted on this projection. This implies that v₃ can vary along the direction orthogonal to the projection plane, as long as its value remains within the flux cone (here depicted as the orange line crossing the cone). Hence, the alternative optima space of this data integration problem consists of alternative vectors, <i>v</i>_{<i>opt(i)</i>}, in which the components <i>v</i>_<i>1</i> and <i>v</i>_<i>2</i> are fixed, and <i>v</i>_<i>3</i> varies between <i>v</i>_{<i>3optmin</i>} and <i>v</i>_{<i>3optmax</i>}.</p

Summary of the alternative optima space of RegrEx_LAD for two case studies, leaf and liver, and four values for the parameter λ.

<p>Summary of the alternative optima space of RegrEx_LAD for two case studies, leaf and liver, and four values for the parameter λ.</p

Alternative optima of CorEx and FastCORE context-specific network extractions.

<p>The results are divided into the leaf-specific scenario for the CorEx (A) and FastCORE (B) alternative optima, and the liver-specific scenario, for CorEx (C), FastCORE (D) and CORDA without applying the metabolic test (E) and applying the metabolic test (F) to further constraint the alternative optima space (see main text). In all cases, non-core reactions are partitioned into the set that is always included in all alternative networks, (the fixed non-core set, in green), the set that is always excluded (excluded non-core, grey) and the variable non-core set (yellow) which is formed by reactions that are included in some of the alternative networks. In both, the leaf- and the liver-specific scenario, the alternative optima networks generated by CorEx contain a larger proportion of fixed non-core reactions and a smaller proportion of variable non-core reactions. These differences in behavior may be explained by the greater number of non-core reactions that are added by FastCORE, as compare to CorEx, in the optimal solution (see main text).</p

Computation time of the evaluated methods.

<p>SEM stands for Standard Error of the Mean.</p><p>^⟳ stands for iteratively repeated.</p><p>*Time is shown in seconds.</p><p>Mean computation times per model extraction, type of mathematical program solved and the used commercial solver are displayed for each evaluated method.</p

Results comparison for different time limits applied to the Gurobi solver.

<p>Four different time limits were evaluated to test the sensitivity of optimal solutions to the early termination criterion (60 s) imposed. In all cases, the λ-value was fixed to a reference optimum, the one obtained when the time limit was 60 s. Mean values for the 11 contexts (with the standard deviation within round brackets) are shown for the correlation between flux values and data, </p><p></p><p></p><p></p><p><mi>ρ</mi></p><mo>-</mo><p></p><p></p><p></p>_(V,D), the mean residual, <p></p><p></p><p></p><p><mi>R</mi></p><mo>-</mo><p></p><p></p><p></p>_(V,D), and the cardinality, <i>i</i>.<i>e</i>., number of reactions of the extracted models, <p></p><p></p><p></p><p><mi>C</mi><mi>a</mi><mi>r</mi><mi>d</mi><mo>.</mo></p><mo>-</mo><p></p><p></p><p></p>.<p></p