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Astaxanthin is a powerful antioxidant known to enhance skin, cardiovascular, eye, and brain health. In this study, the genome insights and astaxanthin production of two newly isolated astaxanthin-producing yeasts (TL35-5 and PL61-2) were evaluated and compared. Based on their phenotypic and genotypic characteristics, TL35-5 and PL61-2 were identified as basidiomycetous yeasts belonging to Rhodotorula paludigena and Rhodotorula sampaioana, respectively. To optimize astaxanthin production, the effects of cultural medium composition and cultivation conditions were examined. The optimal conditions for astaxanthin production in R. paludigena TL35-5 involved cultivation in AP medium containing 10 g/L glucose as the sole carbon source, supplemented with 1.92 g/L potassium nitrate, pH 6.5, and incubation at 20°C for 3 days with shaking at 200 rpm. For R. sampaioana PL61-2, the optimal medium composition for astaxanthin production consisted of AP medium with 40 g/L glucose, supplemented with 0.67 g/L urea, pH 7.5, and the fermentation was carried out at 20°C for 3 days with agitating at 200 rpm. Under their optimal conditions, R. paludigena TL35-5 and R. sampaioana PL61-2 gave the highest astaxanthin yields of 3.689 ± 0.031 and 4.680 ± 0.019 mg/L, respectively. The genome of TL35-5 was 20,982,417 bp in length, with a GC content of 64.20%. A total of 6,789 protein-encoding genes were predicted. Similarly, the genome of PL61-2 was 21,374,169 bp long, with a GC content of 64.88%. It contained 6,802 predicted protein-encoding genes. Furthermore, all essential genes involved in astaxanthin biosynthesis, including CrtE, CrtYB, CrtI, CrtS, and CrtR, were identified in both R. paludigena TL35-5 and R. sampaioana PL61-2, providing evidence for their ability to produce astaxanthin., Competing Interests: The authors declare no competing interests., (Copyright: © 2024 Hoondee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Subunit vaccines feature critical advantages over other vaccine platforms such as stability, price, and minimal adverse effects. To maximize immunological protection of subunit vaccines, adjuvants are considered as main components that are formulated within the subunit vaccine. They can modulate adverse effects and enhance immune outcomes. However, the most suitable formulation providing the best immunological outcomes and safety are still under investigation. In this report, we combined recombinant RBD with human IgG1 Fc to create an RBD dimer. This fusion protein was expressed in CHO and formulated with alternative adjuvants with different immune activation including Montanide ISA51, Poly (I:C), and MPLA/Quil-A® as potential vaccine candidate formulations. Using the murine model, a potent induction of anti-RBD IgG antibodies in immunized mice sera were observed. IgG subclass analyses (IgG1/IgG2a) illustrated that all adjuvanted formulations could stimulate both Th1 and Th2-type immune responses in particular Poly (I:C) and MPLA/Quil-A®, eliciting greater balance. In addition, Montanide ISA51-formulated RBD-Fc vaccination provided a promising level of neutralizing antibodies against live wild-type SARS-CoV-2 in vitro followed by Poly (I:C) and MPLA/Quil-A®, respectively. Also, mice sera from adjuvanted formulations could strongly inhibit RBD:ACE2 interaction. This study offers immunogenicity profiles, forecasted safety based on Vaccine-associated enhanced disease (VAED) caused by Th1-skewed immunity, and neutralizing antibody analysis of candidates of RBD-Fc-based subunit vaccine formulations to obtain an alternative subunit vaccine formulation against SARS-CoV-2., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Laotee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment.