3-Oxocyclo­butane­carboxylic acid: hydrogen bonding in a small-ring γ-keto acid

The title ketocarboxylic acid, C5H6O3, is the smallest carboxy­cyclanone to have its crystal structure determined. It adopts a chiral conformation, by rotation of its carboxyl O atoms away from the plane of skeletal symmetry that passes through the carboxyl carbon and both atoms of the ketone carbonyl. The four-membered ring is non-planar, with a shallow fold of 14.3 (1)° along a line connecting the two α-carbons of the ketone group. In the crystal, the molecules are linked by centrosymmetric hydrogen-bond pairing of ordered carboxylic acid groups [O⋯O = 2.6392 (12) Å and O—H⋯O = 175.74 (15)°], yielding two different sets of dimers, related by by a 21 screw axis in c, in the cell. A C—H⋯O interaction is also present

7-Meth­oxy-3,4-dihydro­naphthalen-1(2H)-one

In the title compound, C11H12O2, the six-membered ketone ring fused to the 7-meth­oxy benzene ring adopts a slightly distorted envelope configuration with the central methyl­ene C atom being the flap. The crystal packing is stabilized by weak inter­molecular C—H⋯O and C—H⋯π inter­actions, which lead to supra­molecular layers in the bc plane

4-Oxocyclo­hexa­neacetic acid: catemeric hydrogen bonding and spontaneous resolution of a single conformational enanti­omer in an achiral ∊-keto acid

The asymmetric unit of the title compound, C8H12O3, consists of a single conformational enanti­omer, which aggregates in the catemeric acid-to-ketone hydrogen-bonding mode [O⋯O = 2.682 (4) Å and O—H⋯O = 172 (6)°]. Four hydrogen-bonding chains of translationally related mol­ecules pass through the cell orthogonal to the 43 screw axis along c, alternating in the 110 and the 10 direction, with alignment with respect to this axis of + + − −. Successive chains are rotated by 90° around the c axis. One C—H⋯O=C close contact, involving the carboxyl group, exists

Redetermination of 3-methyl­benzoic acid

The asymmetric unit of the title compound, C8H8O2, contains two crystallographically independent mol­ecules, which form dimers linked by O⋯H—O hydrogen bonds. The benzene rings in the dimers are inclined at a dihedral angle of 7.30 (8)° and both methyl groups display rotational disorder. This redetermination results in a crystal structure with significantly higher precision than the original determination [Ellas & García-Blanco (1963 ▶). Acta Cryst. 16, 434], in which the authors reported only the unit-cell parameters and space group, without any detailed information on the atomic arrangement. In the crystal, dimers are connected by weak C—H⋯O inter­actions, forming R 2 2(10) and R 4 4(18) rings along [110] and an infinite zigzag chain of dimers along the [001] direction also occurs

Removal of 2-butoxyethanol gaseous emissions by biotrickling filtration packed with polyurethane foam

The removal of 2-butoxyethanol from gaseous emissions was studied using two biotrickling filters (BTF1 and BTF2) packed with polyurethane foam. Two different inoculum sources were used: a pure culture of Pseudomonas sp. BOE200 (BTF1) and activated sludge from a municipal wastewater treatment plant (BTF2). The bioreactors were operated at inlet loads (ILs) of 130 and 195 g m−3 hour−1 and at an empty bed residence time (EBRT) of 12.5 s. Under an IL of ∼130 g m−3 hour−1, BTF1 presented higher elimination capacities (ECs) than BTF2, with average values of 106 ± 7 and 68 ± 8 g m−3 hour−1, respectively. However, differences in ECs between BTFs were decreased by reducing the irrigation intervals from 1 min every 12 min to 1 min every 2 hours in BTF2. Average values of EC were 111 ± 25 and 90 ± 7 g m−3 hour−1 for BTF1 and BTF2, respectively, when working at an IL of ∼195 g m−3 hour−1. Microbial analysis revealed a significant shift in the microbial community of BTF1 inoculated with Pseudomonas sp. BOE200. At the end of the experiment, the species Microbacterium sp., Chryseobacterium sp., Acinetobacter sp., Pseudomonas sp. and Mycobacterium sp. were detected. In BTF2 inoculated with activated sludge, the denaturing gradient gel electrophoresis (DGGE) technique showed a diverse microbial community including species that was able to use 2-butoxyethanol as its carbon source, such as Pseudomonas aeruginosa and Pseudomonas putida as representative species. Although BTF1 inoculated with Pseudomonas sp. BOE200 and higher gas velocity (probably greater gas/liquid mass transfer rate) showed a slight improvement in performance, the use of activated sludge as inoculum seems to be a more feasible option for the industrial application of this technology

Modeling metabolic and signaling pathways in cancer cells

Cancer is a leading cause of death in the world, and the mechanisms that underlie this disease are still not completely understood. As cancer develops and progresses, cells undergo a diversity of mutations that sustain their rapid proliferation and the evasion of the immune system. Cancer cells alter their configuration and organization, exhibiting abnormal phenotypes and changes in functionality. The complexity of cancer lies in their heterogeneity and variability among patients, which challenges the current therapies and drug targets. In the last decades, ten hallmarks of cancer cells have been recognized, including alterations in metabolism and the signaling pathways. The sequencing of the human genome and the advances in omics data processing allowed to generate metabolic and signaling networks for human cells at a genome-scale, enlightening the detailed biochemistry and signal transduction processes occurring in human cells, and enabling to study human metabolism and signaling pathways at a systems level. However, the complexity of these networks hinders a consistent and concise physiological representation. In the field of systems biology, mathematical models and computational methods are derived to describe cellular processes based on experimental data and the biological networks. Furthermore, these models have proven to be valuable in understanding the genotype-phenotype relationship of cells and to formulate new hypotheses to guide experimental design. In this thesis, we present modeling approaches and computational methods to investigate the metabolic and signaling alterations in cancer cells and overcome the challenges arising from biological networks of such size and complexity. Firstly, we curated the thermodynamic properties for all the compounds and reactions in the human metabolic genome-scale models (GEMs) Recon 2 and Recon 3D to guarantee the consistency of the predictions with the bioenergetics of the cell. Moreover, we developed a workflow (redHUMAN) for reconstructing reduced-size models that focus on parts of metabolism relevant to a specific physiology, and we introduce a novel method to account for the cellular interactions with the extracellular medium. Using redHUMAN, we reduced the human GEMs around pathways that are altered in cancer physiology. Secondly, we applied a set of computational methods to integrate omics data into the reduced version of Recon 3D to build metabolic models for breast, colon, and ovarian cancers. These models were used to study how different cancer cells use the metabolic pathways to function and survive and how the underlying genetic deregulations affect the metabolic tasks. Thirdly, we developed a method (CONSIGN) to contextualize signaling networks to a specific type of cell under particular conditions, maximizing the consistency with experimental data. We used this method to generate a breast cancer-specific signaling network for the transcription factor MYC. Finally, we created an integrated model of signaling and metabolic models by accounting for the regulation of metabolic genes by transcription factors. We analyzed the interactions of the MYC signaling network in the breast cancer metabolic model. The work in this thesis demonstrates the potential of metabolic and signaling models to identify and infer the genetic origins and the microenvironment effects in the transformed phenotype of cancer cells, marking a step forward towards the study of drug targets and biomarkers

Systematic reduction of genome-scale models for the study of metabolic phenotypes of human cells

In the last years, the analysis of cellular metabolism has sparked new interest in systems biology and metabolic modelling. In particular, modelling the phenotypes of healthy and diseased cells will help to understand the main metabolic characteristics of disease development and progression. It will also be key to design more effective therapies. The reconstruction of genome-scale models (GEMs) enables the computation of phenotypic traits based on the genetic composition of a target organism. To overcome the well-known challenges when working with large networks, we generate systematically reduced models around specific subsystems. Within this framework, we curate the GEMs to include the thermodynamic properties of the network metabolites and reactions. Furthermore, we consider the composition and utilization of the extracellular-medium metabolites and the synthesis of the biomass precursor metabolites. The reduced models can be used for a broad range of applications extending from omics data integration to kinetic models. We demonstrate here that the study of reduced human GEMs can provide insight and a systematic framework to compare different versions of GEMs (Recon 2 versus Recon 3) as well as different cellular physiologies, such as diseased versus healthy phenotypes

Studying cancer cells phenotypes integrating omics data in human reduced genome-scale metabolic models.

Cancer is a leading cause of death in the world, and the mechanisms underlying this disease are still not completely understood. In the last decades, altered tumour metabolism has been recognized as a hallmark of cancer. This has created a resurgence of interest in the field of systems biology and metabolic modelling to analyse and understand the metabolic changes occurring in cancer cells. Modelling the different phenotypes of healthy and cancer cells will help to make predictions to create effective therapies to prevent, diagnose and treat cancer. The reconstruction of genome-scale models (GEMs) enables the computation of phenotypic traits based on the genetic composition of a target organism. Therefore, we use the human genome-scale model Recon 2 to build cancer cell-specific human GEMs by integrating experimental data (fluxomics, metabolomics, genomics, transcriptomics) and thermodynamic data. To overcome the well-known challenges when working with large networks, we generate systematically reduced models around specific subsystems, considering the composition and usage of the extracellular medium metabolites, and the biosynthesis of the biomass precursor metabolites. Furthermore, we apply pathway enrichment to study the regulation of the pathways under study. The reduced models can be used for a broad range of applications ranging from omics data integration to kinetic models. The proposed pipeline will enhance the comparison and understanding, at the stoichiometric and kinetic level, of the main metabolic differences that emerge in cancer development and progression. Furthermore, predicting the network responses will help to design experiments to find new targets for therapies and drugs

Censo da poboación de lobos (Canis lupus) do norte de Galicia e estimativa da densidade

This work presents the results obtained in wolf population (Canis lupus) surveys addressed in 2019 and 2020 in an area of ​​2,900 km2 located in the northeast of Galicia, where the last official census estimated the reproductive presence of the species in 6 packs. Such surveys consider a minimum potentially breeding population of 22 packs, in 16 of which (72.7%) it was possible to confirm reproduction. These records highlight the need to update the population estimates of the species and, translated into population density, place northeastern Galicia among the regions with the highest density values ​​in the entire global range of the species (4.99 – 11.0 ex. / 100 km2).O presente traballo recolle os resultados obtidos en prospeccións de poboación de lobo (Canis lupus), abordadas en 2019 e 2020 nunha área de 2.900 km2 situada a nordeste de Galicia, onde o último censo oficial cifrou en 6 grupos a presenza reprodutora da especie. Ditas prospeccións sitúan a poboación potencialmente reprodutora nun mínimo de 22 grupos, en 16 dos cales (72.7%) foi posible confirmar reprodución. Estes rexistros poñen en destaque a necesidade de actualización das estimativas de poboación da especie e, traducidos a densidade de poboación, sitúan o nordeste de Galicia cuns valores de densidade estimada dos máis elevados en toda a área de distribución mundial da especie (4.99 – 11.0 ex. /100 km2)