Producción de proteína unicelular de Saccharomyces cerevisiae con granza de arroz e inclusión en cerdos

Cereal flour protein content can be increased by microbial protein action through yeast-driven fermentation. This research was aimed at evaluating the anaerobic fermentation of liquid rice bran with Saccharomyces cerevisiae as a single cell protein (SCP) source for raising pigs. A pilot test for S. cerevisiae SCP production was carried out in the laboratory using rice bran to standardise and find the best process conditions regarding SCP production in a 600 L fermenter tank. The rice bran obtained by fermentation with S. cerevisiae was evaluated for 30 days using twenty-four 21.72±3.31 kg pigs; a randomised complete block design (RCBD) was used, involving four treatments, three blocks and six animals per treatment. The trial had four rice bran post-fermented with S. cerevisiae inclusion levels: T1 0% bran inclusion, T2 10%, T3 20% and T4 25%. Final weight (kg ), average daily gain (g/day), final consumption (kg ) and feed conversion ratio (FCR) were the variables evaluated here. SCP inclusion in food rose from 8.23% to 13.97%. There were significant statistical differences (p0.05) regarding final weight or final consumption. Diet costs were lower for T2, T3 and T4. The anaerobic fermentation of rice bran improved feed protein content through S. cerevisiae growth and can be included in diets for raising pigs; it has good palatability and up to 10% SCP inclusion level, obtaining good yield and a lower cost diet.El contenido proteico de harinas de cereales se incrementa con proteínas microbianas mediante procesos de fermentación utilizando levaduras. El objetivo de esta investigación fue evaluar la inclusión de granza de arroz sometida a fermentación liquida con Saccharomyces cerevisiae por fermentación anaeróbica como fuente de proteína unicelular (PUC) para cerdos en crecimiento. Se realizó prueba piloto en laboratorio para producción de PUC de S. cerevisiae con granza de arroz para estandarizar y encontrar las mejores condiciones en el proceso, para su posterior producción en fermentador de 600 l. Se evalúo la granza obtenida por fermentación con S. cerevisiae por un tiempo de 30 días, en 24 cerdos de 21.72 ± 3.31 kg, distribuidos en un diseño en bloques completos al azar con cuatro tratamientos, tres bloques y seis animales por tratamiento. El ensayo consistió en cuatro niveles de inclusión: T1: 0%, T2: 10%, T3: 20% y T4: 25%, de granza de arroz post-fermentada con S. cerevisiae. Las variables evaluadas fueron peso final (kg ), ganancia de peso (g/día), consumo final (kg ), conversión alimenticia. El alimento pasó de 8.23% a 13.97% de PC. Se presentaron diferencias (p0.05) para el peso final ni el consumo final. El costo de las dietas fue menor para T2, T3 y T4. En conclusión, con la fermentación anaerobia de la granza se mejoró el nivel proteico del alimento por crecimiento de la levadura S. cerevisiae y puede ser incluido en dietas para cerdos en crecimiento, presentándose buena palatabilidad y hasta un nivel de inclusión del 10% obtener buen rendimiento, con una dieta de menor costo

Production of single cell protein of Saccharomyces cerevisiae with broken rice and inclusion in pigs

El contenido proteico de harinas de cereales se incrementa con proteínas microbianas mediante procesos de fermentación utilizando levaduras. El objetivo de esta investigación fue evaluar la inclusión de granza de arroz sometida a fermentación liquida con Saccharomyces cerevisiae por fermentación anaeróbica como fuente de proteína unicelular (PUC) para cerdos en crecimiento. Se realizó prueba piloto en laboratorio para producción de PUC de S. cerevisiae con granza de arroz para estandarizar y encontrar las mejores condiciones en el proceso, para su posterior producción en fermentador de 600 l. Se evalúo la granza obtenida por fermentación con S. cerevisiae por un tiempo de 30 días, en 24 cerdos de 21.72 ± 3.31 kg, distribuidos en un diseño en bloques completos al azar con cuatro tratamientos, tres bloques y seis animales por tratamiento. El ensayo consistió en cuatro niveles de inclusión: T1: 0%, T2: 10%, T3: 20% y T4: 25%, de granza de arroz post-fermentada con S. cerevisiae. Las variables evaluadas fueron peso final (kg), ganancia de peso (g/día), consumo final (kg), conversión alimenticia. El alimento pasó de 8.23% a 13.97% de PC. Se presentaron diferencias (p<0.05) en ganancia diaria de peso T1: 625.81 y T2: 618.77 respecto T3: 526.38 y T4: 542.77 g/día, y en la conversión alimenticia entre T1: 2.20 y T2: 2.16 respecto a los tratamientos T3: 2.54 y T4: 2.45. No hubo diferencias (p>0.05) para el peso final ni el consumo final. El costo de las dietas fue menor para T2, T3 y T4. En conclusión, con la fermentación anaerobia de la granza se mejoró el nivel proteico del alimento por crecimiento de la levadura S. cerevisiae y puede ser incluido en dietas para cerdos en crecimiento, presentándose buena palatabilidad y hasta un nivel de inclusión del 10% obtener buen rendimiento, con una dieta de menor costo.Cereal flour protein content can be increased by microbial protein action through yeastdriven fermentation. This research was aimed at evaluating the anaerobic fermentation of liquid rice bran with Saccharomyces cerevisiae as a single cell protein (SCP) source for raising pigs. A pilot test for S. cerevisiae SCP production was carried out in the laboratory using rice bran to standardise and find the best process conditions regarding SCP production in a 600 L fermenter tank. The rice bran obtained by fermentation with S. cerevisiae was evaluated for 30 days using twenty-four 21.72±3.31 kg pigs; a randomised complete block design (RCBD) was used, involving four treatments, three blocks and six animals per treatment. The trial had four rice bran post-fermented with S. cerevisiae inclusion levels: T1 0% bran inclusion, T2 10%, T3 20% and T4 25%. Final weight (kg), average daily gain (g/day), final consumption (kg) and feed conversion ratio (FCR) were the variables evaluated here. SCP inclusion in food rose from 8.23% to 13.97%. There were significant statistical differences (p<0.05) regarding average daily gain for T1 (625.81 g/day) and T2 (618.77) compared to T3 (526.38) and T4 (542.77) and FCR for T1 (2.20) and T2 (2.16) compared to T3 (2.54) and T4 (2.45). There were no differences (p>0.05) regarding final weight or final consumption. Diet costs were lower for T2, T3 and T4. The anaerobic fermentation of rice bran improved feed protein content through S. cerevisiae growth and can be included in diets for raising pigs; it has good palatability and up to 10% SCP inclusion level, obtaining good yield and a lower cost diet

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)