| 1. |
- Gao, Xiang, et al.
(författare)
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Promoting efficient production of scytonemin in cell culture of Nostoc flagelliforme by periodic short-term solar irradiation
- 2023
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Ingår i: Bioresource Technology Reports. - : Elsevier BV. - 2589-014X. ; 21
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Tidskriftsartikel (refereegranskat)abstract
- The ultraviolet-screening pigment scytonemin is bio-synthesized in some sheathed cyanobacteria, exhibiting important ecological and medicinal values. Scytonemin is recognized to be predominantly induced by ultraviolet (UV)-A/B, but UV radiation is often inhibitory for cyanobacterial biomass increase. Here, we found that short-term shock (within 1 h) of natural sunlight could trigger a persistent production of scytonemin in cell suspension culture of Nostoc flagelliforme for several days. We thus exposed the cultures to solar radiation with different time intervals and durations, and found that everyday 30-min solar irradiation was the most effective for achieving the scytonemin production with less growth inhibition. Besides, the technological potential could be advanced by supplementing NaHCO3 or tryptophan in the cultural medium. This work presents a good example of rationally utilizing environmental solar radiation for effectively producing UV-inducible biochemicals in cyanobacteria.
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| 2. |
- Lappa, Dimitra, 1988, et al.
(författare)
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Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery
- 2023
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203 .- 1932-6203. ; 18:3, s. e0279335-
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Tidskriftsartikel (refereegranskat)abstract
- Weight loss through bariatric surgery is efficient for treatment or prevention of obesity related diseases such as type 2 diabetes and cardiovascular disease. Long term weight loss response does, however, vary among patients undergoing surgery. Thus, it is difficult to identify predictive markers while most obese individuals have one or more comorbidities. To overcome such challenges, an in-depth multiple omics analyses including fasting peripheral plasma metabolome, fecal metagenome as well as liver, jejunum, and adipose tissue transcriptome were performed for 106 individuals undergoing bariatric surgery. Machine leaning was applied to explore the metabolic differences in individuals and evaluate if metabolism-based patients' stratification is related to their weight loss responses to bariatric surgery. Using Self-Organizing Maps (SOMs) to analyze the plasma metabolome, we identified five distinct metabotypes, which were differentially enriched for KEGG pathways related to immune functions, fatty acid metabolism, protein-signaling, and obesity pathogenesis. The gut metagenome of the most heavily medicated metabotypes, treated simultaneously for multiple cardiometabolic comorbidities, was significantly enriched in Prevotella and Lactobacillus species. This unbiased stratification into SOM-defined metabotypes identified signatures for each metabolic phenotype and we found that the different metabotypes respond differently to bariatric surgery in terms of weight loss after 12 months. An integrative framework that utilizes SOMs and omics integration was developed for stratifying a heterogeneous bariatric surgery cohort. The multiple omics datasets described in this study reveal that the metabotypes are characterized by a concrete metabolic status and different responses in weight loss and adipose tissue reduction over time. Our study thus opens a path to enable patient stratification and hereby allow for improved clinical treatments.
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| 3. |
- Li, Peishun, 1988, et al.
(författare)
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Metabolic engineering of human gut microbiome: Recent developments and future perspectives
- 2023
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Ingår i: Metabolic Engineering. - 1096-7176 .- 1096-7184. ; 79, s. 1-13
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Forskningsöversikt (refereegranskat)abstract
- Many studies have demonstrated that the gut microbiota is associated with human health and disease. Manipulation of the gut microbiota, e.g. supplementation of probiotics, has been suggested to be feasible, but subject to limited therapeutic efficacy. To develop efficient microbiota-targeted diagnostic and therapeutic strategies, metabolic engineering has been applied to construct genetically modified probiotics and synthetic microbial consortia. This review mainly discusses commonly adopted strategies for metabolic engineering in the human gut microbiome, including the use of in silico, in vitro, or in vivo approaches for iterative design and construction of engineered probiotics or microbial consortia. Especially, we highlight how genome-scale metabolic models can be applied to advance our understanding of the gut microbiota. Also, we review the recent applications of metabolic engineering in gut microbiome studies as well as discuss important challenges and opportunities.
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| 4. |
- Limeta, Angelo, 1996, et al.
(författare)
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Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors
- 2023
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Ingår i: Computational and Structural Biotechnology Journal. - 2001-0370. ; 21, s. 3912-3919
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Forskningsöversikt (refereegranskat)abstract
- A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.
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| 5. |
- Luo, Hao, 1992, et al.
(författare)
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Modeling the metabolic dynamics at the genome-scale by optimized yield analysis
- 2023
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Ingår i: Metabolic Engineering. - : Elsevier BV. - 1096-7176 .- 1096-7184. ; 75, s. 119-130
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Tidskriftsartikel (refereegranskat)abstract
- The hybrid cybernetic model (HCM) approach is a dynamic modeling framework that integrates enzyme synthesis and activity regulation. It has been widely applied in bioreaction engineering, particularly in the simulation of microbial growth in different mixtures of carbon sources. In a HCM, the metabolic network is decomposed into elementary flux modes (EFMs), whereby the network can be reduced into a few pathways by yield analysis. However, applying the HCM approach on conventional genome-scale metabolic models (GEMs) is still a challenge due to the high computational demands. Here, we present a HCM strategy that introduced an optimized yield analysis algorithm (opt-yield-FBA) to simulate metabolic dynamics at the genome-scale without the need for EFMs calculation. The opt-yield-FBA is a flux-balance analysis (FBA) based method that can calculate optimal yield solutions and yield space for GEM. With the opt-yield-FBA algorithm, the HCM strategy can be applied to get the yield spaces and avoid the computational burden of EFMs, and it can therefore be applied for developing dynamic models for genome-scale metabolic networks. Here, we illustrate the strategy by applying the concept to simulate the dynamics of microbial communities.
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| 6. |
- Zhao, Yonggang, et al.
(författare)
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Whole-genome sequencing reveals high-risk clones of Pseudomonas aeruginosa in Guangdong, China
- 2023
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Ingår i: Frontiers in Microbiology. - 1664-302X. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- The ever-increasing prevalence of infections produced by multidrug-resistant or extensively drug-resistant Pseudomonas aeruginosa is commonly linked to a limited number of aptly-named epidemical 'high-risk clones' that are widespread among and within hospitals worldwide. The emergence of new potential high-risk clone strains in hospitals highlights the need to better and further understand the underlying genetic mechanisms for their emergence and success. P. aeruginosa related high-risk clones have been sporadically found in China, their genome sequences have rarely been described. Therefore, the large-scale sequencing of multidrug-resistance high-risk clone strains will help us to understand the emergence and transmission of antibiotic resistances in P. aeruginosa high-risk clones. In this study, 212 P. aeruginosa strains were isolated from 2 tertiary hospitals within 3 years (2018-2020) in Guangdong Province, China. Whole-genome sequencing, multi-locus sequence typing (MLST) and antimicrobial susceptibility testing were applied to analyze the genomic epidemiology of P. aeruginosa in this region. We found that up to 130 (61.32%) of the isolates were shown to be multidrug resistant, and 196 (92.45%) isolates were Carbapenem-Resistant Pseudomonas aeruginosa. MLST analysis demonstrated high diversity of sequence types, and 18 reported international high-risk clones were identified. Furthermore, we discovered the co-presence of exoU and exoS genes in 5 collected strains. This study enhances insight into the regional research of molecular epidemiology and antimicrobial resistance of P. aeruginosa in China. The high diversity of clone types and regional genome characteristics can serve as a theoretical reference for public health policies and help guide measures for the prevention and control of P. aeruginosa resistance.
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