Development of a genome-scale metabolic model for the lager hybrid yeast S. pastorianus to understand the evolution of metabolic pathways in industrial settings.

In silico tools such as genome-scale metabolic models have shown to be powerful for metabolic engineering of microorganisms. Saccharomyces pastorianus is a complex aneuploid hybrid between the mesophilic Saccharomyces cerevisiae and the cold-tolerant Saccharomyces eubayanus. This species is of biotechnological importance because it is the primary yeast used in lager beer fermentation and is also a key model for studying the evolution of hybrid genomes, including expression pattern of ortholog genes, composition of protein complexes, and phenotypic plasticity. Here, we created the iSP_1513 GSMM for S. pastorianus CBS1513 to allow top-down computational approaches to predict the evolution of metabolic pathways and to aid strain optimization in production processes. The iSP_1513 comprises 4,062 reactions, 1,808 alleles, and 2,747 metabolites, and takes into account the functional redundancy in the gene-protein-reaction rule caused by the presence of orthologous genes. Moreover, a universal algorithm to constrain GSMM reactions using transcriptome data was developed as a python library and enabled the integration of temperature as parameter. Essentiality data sets, growth data on various carbohydrates and volatile metabolites secretion were used to validate the model and showed the potential of media engineering to improve specific flavor compounds. The iSP_1513 also highlighted the different contributions of the parental sub-genomes to the oxidative and non-oxidative parts of the pentose phosphate pathway. Overall, the iSP_1513 GSMM represent an important step toward understanding the metabolic capabilities, evolutionary trajectories, and adaptation potential of S. pastorianus in different industrial settings. IMPORTANCE: Genome-scale metabolic models (GSMM) have been successfully applied to predict cellular behavior and design cell factories in several model organisms, but no models to date are currently available for hybrid species due to their more complex genetics and general lack of molecular data. In this study, we generated a bespoke GSMM, iSP_1513, for this industrial aneuploid hybrid Saccharomyces pastorianus, which takes into account the aneuploidy and functional redundancy from orthologous parental alleles. This model will (i) help understand the metabolic capabilities and adaptive potential of S. pastorianus (domestication processes), (ii) aid top-down predictions for strain development (industrial biotechnology), and (iii) allow predictions of evolutionary trajectories of metabolic pathways in aneuploid hybrids (evolutionary genetics).

Risk factors for severe disease and mortality in children with COVID-19.

Background: Pediatric COVID-19 patients have lower rates of hospitalization and fatal outcomes compared to adults with COVID-19; however, children represent a challenge in the detection, diagnosis, and treatment of COVID-19. Our aim was to determine the risk factors for hospital admission, invasive mechanical ventilation, and mortality in pediatric COVID-19 patients in Mexico during the COVID-19 pandemic. Material and methods: A retrospective cohort of pediatric patients with COVID-19 from February 2020 to April 2021 was reported on the National Epidemiological Surveillance System for Viral Respiratory Disease (SISVER) platform. Results: Among the 104,133 patients included in our study, 6214 were hospitalized, and 621 patients underwent invasive mechanical ventilation. A total of 0.65 % died during hospitalization. Children aged <12 months (odds ratio [OR]: 17.1; 95 % confidence interval [CI]: 15.9-19.4, p < 0.001), 1-4 years (OR: 3.69; 95 % CI: 3.2-4.1, p < 0.001), 5-9 years (OR: 1.86; 95 % CI: 1.66-2.08, p < 0.001), and 10-14 years (OR: 1.23; 95 % CI: 1.11-1.37, p < 0.001), and those diagnosed with diabetes (OR: 2.32; 95 % CI 1.68-3.20, p < 0.001) and obesity (OR: 1.24; 95 % CI 1.04-1.48, p = 0.015) were associated with hospital admission. Renal disease (OR: 3.85; 95 % CI: 2.25-6.59, p < 0.001) was associated with invasive mechanical ventilation. Pneumonia (OR: 15.9; 95 % CI: 12.6-20.1, p < 0.001) and renal disease (OR: 3.85; 95 % CI: 2.25-6.59, p value < 0.001) were associated with death. Conclusion: Pneumonia increases the risk of death. The youngest age group has a higher risk of hospital admission. Comorbidities such as renal disease or immunosuppression increase the risk of death in all age groups.

The Association between Body Composition Phenotype and Insulin Resistance in Post-COVID-19 Syndrome Patients without Diabetes: A Cross-Sectional, Single-Center Study.

BACKGROUND: The most frequent body composition alterations in post-COVID-19 syndrome include low muscle mass, dynapenia, sarcopenia, and obesity. These conditions share interconnected pathophysiological mechanisms that exacerbate each other. The relationship between body composition phenotypes and metabolic abnormalities in post-COVID-19 syndrome remains unclear. OBJECTIVE: To evaluate the association between body composition phenotypes and insulin resistance (IR) and metabolic abnormalities in non-diabetic individuals with post-COVID-19 syndrome. METHODS: A cross-sectional, single-center study involving 483 subjects with post-COVID-19 syndrome following moderate to severe acute COVID-19 requiring hospitalization. Individuals with diabetes, those who declined to participate, or those who could not be contacted were excluded. Body composition phenotypes were classified as normal weight, dynapenia, sarcopenia, dynapenic obesity, and sarcopenic obesity (SO). RESULTS: The average age was 52.69 ± 14.75 years; of note, 67.08% were male. The prevalence of body composition phenotypes was as follows: 13.25% were of normal weight, 9.52% had dynapenia, 9.94% had sarcopenia, 43.69% had obesity, 18.84% had dynapenic obesity, and 4.76% had SO. Additionally, 58.18% had IR. Obesity (OR: 2.98, CI95%; 1.64-5.41) and dynapenic obesity (OR: 4.98, CI95%; 1.46-6.88) were associated with IR. CONCLUSION: The most common body composition phenotypes were obesity, dynapenic obesity, and dynapenia. Furthermore, obesity and dynapenic obesity were associated with IR in post-COVID-19 syndrome.

Immunonutrition in cervical cancer: immune response modulation by diet

ABSTRACT In the development of cervical cancer (CC), the immune response plays an essential role, from the elimination of human papillomavirus (HPV) infection to the response against the tumor. For optimal function of the immune response, various factors are required, one of the most important being an adequate nutrition. The complex interaction between nutrients and microbiota maintains the immune system in homeostasis and in case of infection, it provides the ability to fight against pathogen invasion, as occurs in HPV infection. The purpose of this article is to describe the role of diet, food, and specific nutrients in the immune response from the onset of infection to progression to precancerous lesions and CC, as well as the role of diet and nutrition during oncological treatment. The immunomodulatory role of microbiota is also discussed. A detailed analysis of the evidence leads us to recommend a nutritional pattern very similar to the Mediterranean diet or the prudent diet for an optimal immune response. Moreover, pre- and probiotics favorably modulate the microbiota and induce preventive and therapeutic effects against cancer.