Estudio de cuatro cepas nativas de microalgas para evaluar su potencial uso en la producción de biodiesel

La presente investigación evaluó la composición y capacidad de acumulación de lípidos en cuatro microalgas nativas de Colombia y un alga de referencia como fuentes potenciales para la producción de biodiesel. Las microalgas Scenedemus ovalternus y Chlorella vulgaris presentaron las mayores productividades de lípidos con 18,8 y 18,7mg•L-1•día-1, respectivamente, equivalentes a 4,1 veces la productividad de aceite de la palma africana, actual materia prima empleada en Colombia para la producción industrial de biodiesel. De acuerdo con la caracterización de los ácidos grasos producidos por las microalgas estudiadas, todas pueden ser empleadas en la producción de biodiesel, debido a la similitud de estos con aceites ya empleados en la producción de biodiesel, por lo cual fue escogida la microalga Chlorella vulgaris para estudios posteriores, los cuales consistieron en la optimización de la acumulación y productividad de lípidos variando los factores contenido de CO2, irradiancia, fotoperiodo y aireación. La productividad de lípidos óptima predicha por el modelo estadístico dentro del intervalo estudiado fue 69,7 ± 5,9 7mg.L-1.día-1, 15,2 veces la productividad de la palma africana, para un contenido de CO2 de 2%, irradiancia de 114 μE•m-2•s-1, fotoperiodo de 24:0 LO y aireación de 1,2 vvm; el contenido de lípidos bajo las condiciones mencionadas fue de 16,4 ± 1,4%. La optimización de los factores para maximizar el contenido de lípidos y minimizar la disminución de la productividad de lípidos se logró para un contendido de CO2 del 1,2%, irradiancia de 22 μE•m-2•s-1, fotoperiodo de 12:12 LO y aireación de 0,4 vvm con valores de 32,7 ± 1,4% y 42,0 ± 5,9 mg•L-1•día-1, respectivamente, equivalente a 9,1 veces la productividad de aceite de la palma africana, las anteriores son condiciones fácilmente alcanzables en cualquier parte del territorio colombiano. / Abstract. This research evaluated the ability to accumulate lipids and their composition of five microalgae, four of them native from Colombia and one reference alga as potential feedstocks for biodiesel production. The microalgae Scenedesmus ovalternus and Chlorella vulgaris had the highest lipid productivity: 18.8 and 18.7 mg•L-1•day-1, respectively, 4.1 times longer than the productivity of palm oil, current feedstock for the industrial production of biodiesel in Colombia. According to the characterization of the fatty acids produced by the microalgae studied, the five microalgae can be employed in the production of biodiesel because their oils have similarity with the oils used in the production of biodiesel. Microalga Chlorella vulgaris was chosen for next research where CO2 content, irradiance, photoperiod and aeration were evaluated as parameters of production and accumulation of lipids. The optimal lipid productivity predicted by the statistical model in the range studied was 69.7 ± 5.9 mg•L-1•day-1, 15.2 times the oil productivity of African Palm, for CO2 content of 2%, irradiance of 114 μE•m-2•s-1, photoperiod of 24:0 LO and aeration of 1.2 vvm. The content of lipids in those conditions was 16.4 ± 1.4%. The optimal conditions of the factors in order to maximize the lipid content and to minimize the reduction in lipid productivity was reached with CO2 content 1.2%, photoperiod 12:12 LO, Irradiance 22 μE•m-2•s-1 and aeration 0.4 vvm, these conditions are easily reachable in any part of Colombia. The optimum lipid contend and lipid productivity were 32.7 ± 1.4% and 42.0 ± 5.9 mg•L-1•day-1, respectively, 9.1 times the oil productivity of African PalmMaestrí

TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse

Background: During the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based on transparent, homogeneous, and formal methodologies, making them understandable, reproducible, and auditable. Objectives: This study aims to design and implement a methodology, according with FAIR Principles, for building ETL processes (focused on data extraction, selection, and transformation) for EHR reuse in a transparent and flexible manner, applicable to any clinical condition and health care organization. Methods: The proposed methodology comprises four stages: (1) analysis of secondary use models and identification of data operations, based on internationally used clinical repositories, case report forms, and aggregated datasets; (2) modeling and formalization of data operations, through the paradigm of the Detailed Clinical Models; (3) agnostic development of data operations, selecting SQL and R as programming languages; and (4) automation of the ETL instantiation, building a formal configuration file with XML. Results: First, four international projects were analyzed to identify 17 operations, necessary to obtain datasets according to the specifications of these projects from the EHR. With this, each of the data operations was formalized, using the ISO 13606 reference model, specifying the valid data types as arguments, inputs and outputs, and their cardinality. Then, an agnostic catalog of data was developed through data-oriented programming languages previously selected. Finally, an automated ETL instantiation process was built from an ETL configuration file formally defined. Conclusions: This study has provided a transparent and flexible solution to the difficulty of making the processes for obtaining EHR-derived data for secondary use understandable, auditable, and reproducible. Moreover, the abstraction carried out in this study means that any previous EHR reuse methodology can incorporate these results into them.Ministerio de Economía y Competitividad Instituto de Salud Carlos III PI18/00981, PI18/01047, PI18CIII/00019.S

Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO2 levels

The increase in atmospheric CO2 due to anthropogenic activities is generating climate change, which has resulted in a subsequent rise in global temperatures with severe environmental impacts. Biological mitigation has been considered as an alternative for environmental remediation and reduction of greenhouse gases in the atmosphere. In fact, the use of easily adapted photosynthetic organisms able to fix CO2 with low-cost operation is revealing its high potential for industry. Among those organism, the algae Chlamydomonas reinhardtii have gain special attention as a model organism for studying CO2 fixation, biomass accumulation and bioenergy production upon exposure to several environmental conditions. In the present study, we studied the Chlamydomonas response to different CO2 levels by comparing metabolomics and transcriptomics data with the predicted results from our new-improved genomic-scale metabolic model. For this, we used in silico methods at steady dynamic state varying the levels of CO2. Our main goal was to improve our capacity for predicting metabolic routes involved in biomass accumulation. The improved genomic-scale metabolic model presented in this study was shown to be phenotypically accurate, predictive, and a significant improvement over previously reported models. Our model consists of 3726 reactions and 2436 metabolites, and lacks any thermodynamically infeasible cycles. It was shown to be highly sensitive to environmental changes under both steady-state and dynamic conditions. As additional constraints, our dynamic model involved kinetic parameters associated with substrate consumption at different growth conditions (i.e., low CO2-heterotrophic and high CO2-mixotrophic). Our results suggest that cells growing at high CO2 (i.e., photoautotrophic and mixotrophic conditions) have an increased capability for biomass production. In addition, we have observed that ATP production also seems to be an important limiting factor for growth under the conditions tested. Our experimental data (metabolomics and transcriptomics) and the results predicted by our model clearly suggest a differential behavior between low CO2-heterotrophic and high CO2-mixotrophic growth conditions. The data presented in the current study contributes to better dissect the biological response of C. reinhardtii, as a dynamic entity, to environmental and genetic changes. These findings are of great interest given the biotechnological potential of this microalga for CO2 fixation, biomass accumulation, and bioenergy production

Efecto del hierro en el crecimiento y acumulación de lípidos en la microalga colombiana Chlorella Vulgaris LAUN 0019

En este trabajo se evalúa el efecto del ión ferroso (Fe2+) sobre el crecimiento y acumulación de lípidos totales de la microalga Chlorella vulgaris. Se empleó medio Bristol estándar para su cultivo; la cinética de crecimiento se midió por conteo directo y la determinación de lípidos totales se realizó mediante extracción con Soxhlet. Se estudiaron cinco diferentes concentraciones de este ión, entre 2,16 μM y 50,0 μM. El medio enriquecido con una concentración de 10,0 μM produjo la máxima velocidad específica de crecimiento celular (0,76 día-1), mientras que las máximas productividades de biomasa y de lípidos se presentaron a la concentración 5,00 μM con valores de 112,4 mg·L-1·día-1 y 6,52 mg·L-1·día-1 respectivamente. Para las concentraciones más altas de hierro (21,5 y 50,0 μM), la microalga presentó inhibición por sustrato. Finalmente, para concentraciones menores que 10,0 μM se encontró que para una significancia del 5% la concentración del hierro no afecta significativamente la productividad de biomasa y lípidos

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

RICORS2040 : The need for collaborative research in chronic kidney disease

Chronic kidney disease (CKD) is a silent and poorly known killer. The current concept of CKD is relatively young and uptake by the public, physicians and health authorities is not widespread. Physicians still confuse CKD with chronic kidney insufficiency or failure. For the wider public and health authorities, CKD evokes kidney replacement therapy (KRT). In Spain, the prevalence of KRT is 0.13%. Thus health authorities may consider CKD a non-issue: very few persons eventually need KRT and, for those in whom kidneys fail, the problem is 'solved' by dialysis or kidney transplantation. However, KRT is the tip of the iceberg in the burden of CKD. The main burden of CKD is accelerated ageing and premature death. The cut-off points for kidney function and kidney damage indexes that define CKD also mark an increased risk for all-cause premature death. CKD is the most prevalent risk factor for lethal coronavirus disease 2019 (COVID-19) and the factor that most increases the risk of death in COVID-19, after old age. Men and women undergoing KRT still have an annual mortality that is 10- to 100-fold higher than similar-age peers, and life expectancy is shortened by ~40 years for young persons on dialysis and by 15 years for young persons with a functioning kidney graft. CKD is expected to become the fifth greatest global cause of death by 2040 and the second greatest cause of death in Spain before the end of the century, a time when one in four Spaniards will have CKD. However, by 2022, CKD will become the only top-15 global predicted cause of death that is not supported by a dedicated well-funded Centres for Biomedical Research (CIBER) network structure in Spain. Realizing the underestimation of the CKD burden of disease by health authorities, the Decade of the Kidney initiative for 2020-2030 was launched by the American Association of Kidney Patients and the European Kidney Health Alliance. Leading Spanish kidney researchers grouped in the kidney collaborative research network Red de Investigación Renal have now applied for the Redes de Investigación Cooperativa Orientadas a Resultados en Salud (RICORS) call for collaborative research in Spain with the support of the Spanish Society of Nephrology, Federación Nacional de Asociaciones para la Lucha Contra las Enfermedades del Riñón and ONT: RICORS2040 aims to prevent the dire predictions for the global 2040 burden of CKD from becoming true

The Helicobacter pylori Genome Project : insights into H. pylori population structure from analysis of a worldwide collection of complete genomes

Helicobacter pylori, a dominant member of the gastric microbiota, shares co-evolutionary history with humans. This has led to the development of genetically distinct H. pylori subpopulations associated with the geographic origin of the host and with differential gastric disease risk. Here, we provide insights into H. pylori population structure as a part of the Helicobacter pylori Genome Project (HpGP), a multi-disciplinary initiative aimed at elucidating H. pylori pathogenesis and identifying new therapeutic targets. We collected 1011 well-characterized clinical strains from 50 countries and generated high-quality genome sequences. We analysed core genome diversity and population structure of the HpGP dataset and 255 worldwide reference genomes to outline the ancestral contribution to Eurasian, African, and American populations. We found evidence of substantial contribution of population hpNorthAsia and subpopulation hspUral in Northern European H. pylori. The genomes of H. pylori isolated from northern and southern Indigenous Americans differed in that bacteria isolated in northern Indigenous communities were more similar to North Asian H. pylori while the southern had higher relatedness to hpEastAsia. Notably, we also found a highly clonal yet geographically dispersed North American subpopulation, which is negative for the cag pathogenicity island, and present in 7% of sequenced US genomes. We expect the HpGP dataset and the corresponding strains to become a major asset for H. pylori genomics

Análisis de balance de flujo dinámico de la producción de 1,3-Propanodiol a partir de Clostridium sp

Resumen El incremento en el glicerol obtenido como coproducto del biodiesel ha incentivado la producción de nuevos productos industriales como el 1,3-propanodiol (PDO) mediante transformación biotecnológica usando bacterias como Clostridium butyricum. Ahora bien, a pesar del creciente interés de este proceso de bioproducción, el metabolismo de Clostridium butyricum prácticamente no ha sido modelado. Por lo tanto, fue reconstruido el primer modelo metabólico de escala genómica (modelo GSM) de una cepa de Clostridium productora de PDO (iCbu641), el cual contiene 641 genes, 365 enzimas, 891 reacciones y 701 metabolitos. Fue lograda una predicción en la expresión de enzimas del 83% después de comparación entre datos de proteomica y distribución de fluxes predichos mediante Análisis de Balance de Flujo (FBA). Las restantes enzimas no predichas están direccionalmente acopladas al crecimiento de acuerdo a la Búsqueda de Fluxes Enlazados (FCF) y muchas de ellas están involucradas en procesos que el FBA no puede anticipar, tales como mecanismos de regulación celular. En adición, durante la validación usando datos de fermentación en estado estacionario, diferentes estados fenotípicos fueron observados dependiendo de la concentración de glicerol, los cuales fueron predichos mediante diferentes funciones objetivo. Posteriormente, fue desarrollada una aproximación dinámica que fue validada experimentalmente mediante cultivos de la cepa nativa Clostridium sp. IBUN 158B. Además fue realizado análisis de sensibilidad detectando que la restricción cinética de consumo de glicerol fue el principal parámetro que afectó la predicción de PDO producido. Los restantes parámetros evaluados en el análisis de sensibilidad fueron empleados en el desarrollo de un modelo segregado, el cual permitió predecir comportamientos poblacionales. Por otra parte, perturbaciones en el modelo GSM fueron realizadas mediante deleciones de enzimas, sin embargo ninguna mutante evaluada tuvo incrementos significativos en la producción de PDO, lo cual es debido a que el PDO es un metabolito primario y su producción también es optimizada por el FBA. Seguidamente, simulaciones dinámicas adicionales fueron realizadas con el objetivo de predecir cultivos por lote alimentado, permitiendo mejorar la producción de PDO de 23.5 hasta 66g/L. Finalmente, gracias a que los valores predichos estuvieron de acuerdo con valores experimentales, es posible sugerir que el modelo reconstruido puede ser empleado para proponer nuevos escenarios y por tanto reducir tiempo y costos asociados a la experimentación. ///Abstract. The increase of glycerol obtained as a byproduct of biodiesel has encouraged the production of new industrial products such as 1,3-propanediol (PDO) using biotechnological transformation via bacteria like Clostridium butyricum. Despite this increasing role as bio-production platforms, Clostridium butyricum metabolism remains poorly modeled. Herein, it was reconstructed the first genome-scale metabolic (GSM) model of a PDO producer Clostridium strain (iCbu641), which contains 641 genes, 365 enzymes, 891 reactions and 701 metabolites. It was found an enzyme expression prediction near to 83% after comparison between proteomic data and flux distribution estimated using Flux Balance Analysis (FBA). The remaining unpredicted enzymes are directionally coupled to growth according to Flux Coupling Finding (FCF) and many of them are involved in processes that FBA cannot anticipate as cellular regulatory mechanisms. Additionally, in the validation using steady state fermentation data, different phenotypes states were observed depending glycerol concentration, which were predicted using different objective functions. Also, a dynamic approach was developed and validated experimentally through cultures of the native strain Clostridium sp IBUN 158B. Furthermore, sensitivity analyses were made detecting the kinetic constraint of glycerol uptake as the main parameter affecting the PDO prediction. The remaining parameters considered in the sensitivity analysis were employed to develop a segregate model, which allowed predicting population behavior. Moreover, perturbations in GSM through enzyme deletions were evaluated, however no significant increase in PDO production was predicted, which is due PDO is a primary metabolite and its production is optimized by FBA. Additional dynamic simulations were made in order to predict fedbatch cultures, allowing to enhance the PDO produced from 23.5 up to 66g/L. Finally, the agreement between predicted and experimental values allows to propose new scenarios using the model reconstructed and therefore reducing time and costs associated to experimentation.Doctorad

Clostridium butyricum population balance model: Predicting dynamic metabolic flux distributions using an objective function related to extracellular glycerol content.

BACKGROUND:Extensive experimentation has been conducted to increment 1,3-propanediol (PDO) production using Clostridium butyricum cultures in glycerol, but computational predictions are limited. Previously, we reconstructed the genome-scale metabolic (GSM) model iCbu641, the first such model of a PDO-producing Clostridium strain, which was validated at steady state using flux balance analysis (FBA). However, the prediction ability of FBA is limited for batch and fed-batch cultures, which are the most often employed industrial processes. RESULTS:We used the iCbu641 GSM model to develop a dynamic flux balance analysis (DFBA) approach to predict the PDO production of the Colombian strain Clostridium sp IBUN 158B. First, we compared the predictions of the dynamic optimization approach (DOA), static optimization approach (SOA), and direct approach (DA). We found no differences between approaches, but the DOA simulation duration was nearly 5000 times that of the SOA and DA simulations. Experimental results at glycerol limitation and glycerol excess allowed for validating dynamic predictions of growth, glycerol consumption, and PDO formation. These results indicated a 4.4% error in PDO prediction and therefore validated the previously proposed objective functions. We performed two global sensitivity analyses, finding that the kinetic input parameters of glycerol uptake flux had the most significant effect on PDO predictions. The other input parameters evaluated during global sensitivity analysis were biomass composition (precursors and macromolecules), death constants, and the kinetic parameters of acetic acid secretion flux. These last input parameters, all obtained from other Clostridium butyricum cultures, were used to develop a population balance model (PBM). Finally, we simulated fed-batch cultures, predicting a final PDO production near to 66 g/L, almost three times the PDO predicted in the best batch culture. CONCLUSIONS:We developed and validated a dynamic approach to predict PDO production using the iCbu641 GSM model and the previously proposed objective functions. This validated approach was used to propose a population model and then an increment in predictions of PDO production through fed-batch cultures. Therefore, this dynamic model could predict different scenarios, including its integration into downstream processes to predict technical-economic feasibilities and reducing the time and costs associated with experimentation