Biology subjects

Sommer, M. O. A.

Publications and source records attributed to Sommer, M. O. A..

13 recordsLinked to original sources

bioRxiv2026

Saccharomyces boulardii attenuates obesity-associated inflammation and weight gain through coordinated gut ecosystem remodeling

The gut microbiome is a key regulator of metabolic homeostasis and contributes to obesity progression through effects on immune signaling, gut barrier integrity, and systemic inflammation. Microbiome-targeted strategies are therefore being explored as complementary approaches to conventional weight-loss therapies. Here, we investigated the probiotic yeast Saccharomyces boulardii in a murine model of diet-induced obesity (DIO) using an integrated multi-omics framework combining metabolic phenotyping, gut microbiome profiling, cecal metabolomics, colonic transcriptomics, and portal cytokine analysis. S. boulardii reduced food intake, attenuated weight gain, and increased energy expenditure without major changes in circulating metabolic hormone levels. Microbial diversity remained largely preserved, but selective enrichment of Bacteroidales lineages, including Muribaculaceae, was observed alongside functional remodeling of microbial pathways. Cecal metabolomics revealed increased B-vitamins, betaine, and GABA, with reduced stress-associated metabolites. Colonic transcriptomics showed attenuation of TNF/NF-{kappa}B signaling and enrichment of interferon and epithelial programs, while portal cytokine profiling indicated reduced inflammatory chemokines with trends toward increased IL-17A and IL-22. Integrated multi-omics analysis identified coordinated host-microbe interactions across metabolic, transcriptional, and immune layers. Collectively, these findings demonstrate that S. boulardii modulates the gut-immune-metabolic axis in obesity, supporting microbiome-based interventions as potential adjunct strategies targeting metabolic inflammation.

microbiology↗

bioRxiv2023

A Fully Defined Synthetic Medium Mimicking Sugar Cane Molasses

BackgroundYeast research in the context of food/beverage production and industrial biotechnology faces a dilemma: to use real industrial media or to use fully defined laboratory media? While the former option might lead to experiments closer to industrial conditions, the latter has the advantage of allowing for reproducibility and comparability of results among different laboratories, as well as being suitable for the investigation of how different individual components affect microbial or process performance. It is undoubtable that the development of a synthetic must a few decades ago led to important advances in wine yeast research. ResultsWe developed a fully defined medium that mimics sugarcane molasses, a frequently used medium in different industrial processes where yeast is cultivated. The medium, named 2SMol, builds upon a previously published semi-defined formulation and is conveniently prepared from some stock solutions: C-source, organic N, inorganic N, organic acids, trace elements, vitamins, Mg+K, and Ca. We validated the 2SMol recipe in a scaled-down sugarcane biorefinery model, comparing the performance of different yeast strains in different real molasses-based media. We also showcase the flexibility of the medium by investigating the effect of nitrogen availability on the ethanol yield during fermentation. ConclusionsHere we present in detail the development of a fully defined synthetic molasses medium, and we hope the 2SMol formulation will be valuable to researchers both in academia and industry to obtain new insights and developments in industrial yeast biotechnology.

bioengineering↗

bioRxiv2022

Biocontainment strategies for in vivo applications of Saccharomyces boulardii

The human gastrointestinal tract is a complex and dynamic environment, playing a crucial role in human health. Microorganisms engineered to express a therapeutic activity have emerged as a novel modality to manage numerous diseases. Such advanced microbiome therapeutics (AMTs) must be contained within the treated individual. Hence safe and robust biocontainment strategies are required to prevent the proliferation of microbes outside the treated individual. Here we present the first biocontainment strategy for a probiotic yeast, demonstrating a multilayered strategy combining an auxotrophic and environmental-sensitive strategy. We knocked out the genes THI6 and BTS1, causing thiamine auxotrophy and increased sensitivity to cold, respectively. The biocontained Saccharomyces boulardii was unable to grow in the absence of thiamine above 1 ng/mL and exhibited a severe growth defect at temperatures below 20{degrees}C. The biocontained strain was well tolerated and viable in mice and demonstrated equal efficiency in peptide production as the ancestral non-biocontained strain. In combination, the data support that thi6{Delta} and bts1{Delta} enable biocontainment of S. boulardii, which could be a relevant chassis for future yeast-based AMTs.

synthetic biology↗

bioRxiv2022

Oral delivery of GLP-1R agonist by an engineered probiotic yeast strain has anti-obesity effects in mice

Obesity is rapidly increasing within the global population and is one of the leading causes of chronic diseases, including type 2 diabetes (T2D), non-alcoholic fatty liver disease, and cardiovascular diseases. Glucagon-like peptide-1 receptor (GLP-1R) agonists have emerged as promising therapeutic agents for treating T2D and obesity. However, the route of administration of the GLP-1R agonists is currently by injection or high oral dosages of the therapeutic combined with absorption enhancers. Oral delivery of GLP-1R agonists remains the preferred administration route due to convenience and high patient compliance. Thus, strategies to improve the oral delivery of this therapeutic are needed. In this study, we engineered the probiotic yeast Saccharomyces boulardii strain to produce Exendin-4, a GLP-1R agonist, in the gastrointestinal tract to reduce the adverse effects of diet-induced obesity in male C57BL/6 mice. The biological efficiency of the secreted Exendin-4 from S. boulardii was characterised ex vivo on isolated pancreatic islets, demonstrating induced insulin secretion. Furthermore, in vivo characterisation of the engineered strain identified a synergistic effect of cold exposure and Sb-Exe4 by successfully inhibiting appetite and promoting body weight loss under cold exposure (8{degrees}C). In addition, the combination of cold and Sb-Exe4 improved the glucose and lipid homeostasis in the mice by increasing the circulating glucagon level and reducing the inflammatory marker TNF-. Our results demonstrate that S. boulardii can be genetically modified to secrete and deliver active therapeutic GLP-1R agonists in the gastrointestinal tract improving the metabolism of the host.

synthetic biology↗

bioRxiv2021

Niclosamide shows strong antiviral activity in a human airway model of SARS-CoV-2 infection and a conserved potency against the UK B.1.1.7 and SA B.1.351 variant

SARS-CoV-2 variants are emerging with potential increased transmissibility highlighting the great unmet medical need for new therapies. Niclosamide is a potent anti-SARS-CoV-2 agent that has advanced in clinical development. We validate the potent antiviral efficacy of niclosamide in a SARS-CoV-2 human airway model. Furthermore, niclosamide is effective against the D614G, B.1.1.7 and B.1.351 variants. Our data further support the potent anti-SARS-CoV-2 properties of niclosamide and highlights its great potential as a therapeutic agent for COVID-19.

microbiology↗

bioRxiv2021

Strain dynamics of specific contaminant bacteria modulate the performance of ethanol biorefineries

Bioethanol is a viable alternative for fossil fuels, and its use has lowered CO2 emissions by over 500 million tonnes in Brazil alone by replacing more than 40% of the national gasoline consumption. However, contaminant bacteria reduce yields during fermentation. Our understanding of these contaminants is limited to targeted studies, and the interplay of the microbial community and its impact on fermentation efficiency remains poorly understood. Comprehensive surveying and longitudinal analysis using shotgun metagenomics of two major biorefineries over a production season revealed similar patterns in microbial community structure and dynamics throughout the entire fermentation system. Strain resolution metagenomics identified specific Lactobacillus fermentum strains as strongly associated with poor industrial performance and laboratory-scale fermentations revealed yield reductions of up to 4.63{+/-}1.35% depending on the specific contaminating strains. Selective removal of these strains could reduce emissions from the bioethanol industry by more than 2x106 tonnes per year. Using the large-scale Brazilian ethanol fermentations as a model system for studying microbiome-phenotype relationships this study further demonstrates how high-resolution metagenomics can identify culprits of large scale industrial biomanufacturing.

microbiology↗

bioRxiv2020

A Novel Efficient L-Lysine Exporter Identified by Functional Metagenomics

Lack of active export system often limits the industrial bio-based production processes accumulating the intracellular product and hence complexing the purification steps. L-lysine, an essential amino acid, is produced biologically in quantities exceeding two million tons per year; yet, L-lysine production is challenged by efficient export system at high titres during fermentation. To address this issue, new exporter candidates for efficient efflux of L-lysine are needed. Using metagenomic functional selection, we identified 58 genes encoded on 28 unique metagenomic fragments from cow gut microbiome library that improved L-lysine tolerance. These genes include a novel putative L-lysine transporter, belonging to a previously uncharacterized EamA superfamily. Characterization using Xenopus oocyte expression system as well as an Escherichia coli host demonstrates activity as a L-lysine transporter. This novel exporter improved L-lysine tolerance in E. coli by 40% and enhanced the specific productivity of L-lysine in an industrial Corynebacterium glutamicum strain by 12%. Our approach allows the sequence-independent discovery of novel exporters and can be deployed to increase titres and productivity of toxicity-limited bioprocesses.

molecular biology↗

bioRxiv2020

Regulatory control circuits for stabilizing long-term anabolic product formation in yeast

Engineering living cells for production of chemicals, enzymes and therapeutics can burden cells due to use of limited native co-factor availability and/or expression burdens, totalling a fitness deficit compared to parental cells encoded through long evolutionary trajectories to maximise fitness. Ultimately, this discrepancy puts a selective pressure against fitness-burdened engineered cells under prolonged bioprocesses, and potentially leads to complete eradication of high-performing engineered cells at the population level. Here we present the mutation landscapes of fitness-burdened yeast cells engineered for vanillin-{beta}-glucoside production. Next, we design synthetic control circuits based on transcriptome analysis and biosensors responsive to vanillin-{beta}-glucoside pathway intermediates in order to stabilize vanillin-{beta}-glucoside production over [~]55 generations in sequential passage experiments. Furthermore, using biosensors with two different modes of action we identify control circuits linking vanillin-{beta}-glucoside pathway flux to various essential cellular functions, and demonstrate control circuits robustness and 92% higher vanillin-{beta}-glucoside production, including 5-fold increase in total vanillin-{beta}-glucoside pathway metabolite accumulation, in a fed-batch fermentation compared to vanillin-{beta}-glucoside producing cells without control circuits.

synthetic biology↗

bioRxiv2019

Adaptive laboratory evolution reveals general and specific chemical tolerance mechanisms and enhances biochemical production

Tolerance to high product concentrations is a major barrier to achieving economically viable processes for bio-based chemical production. Chemical tolerance mechanisms are often unknown, thus their rational design is not achievable. To reveal unknown tolerance mechanisms we used an automated platform to evolve Escherichia coli to grow in previously toxic concentrations of 11 chemicals that have applications as polymer precursors, chemical intermediates, or biofuels. Re-sequencing of isolates from 88 independently evolved populations, reconstruction of mutations, and cross-compound tolerance profiling was employed to uncover general and specific tolerance mechanisms. We found that: 1) the broad tolerance of strains towards chemicals varied significantly depending on the chemical stress condition under which the strain was evolved; 2) the strains that acquired high levels of NaCl tolerance also became broadly tolerant to most chemicals; 3) genetic tolerance mechanisms included alterations in regulatory, cell wall, transcriptional and translational functions, as well as more chemical-specific mechanisms related to transport and metabolism; 4) using pre-tolerized starting strains can significantly enhance subsequent production of chemicals when a production pathway is inserted; and 5) only a subset of the evolved isolates showed improved production indicating that this approach is especially useful when a large number of independently evolved isolates are screened for production. We provide a comprehensive genotype-phenotype map based on identified mutations and growth phenotypes for 224 chemical tolerant strains.

microbiology↗

bioRxiv2019

Antibiotic treatment drives the diversification of the human gut resistome

Despite the documented antibiotic-induced disruption of the gut microbiota, the impact of antibiotic intake on strain-level dynamics, evolution of resistance genes, and factors influencing resistance dissemination potential remains poorly understood. To address this gap we analyzed public metagenomic datasets from 24 antibiotic treated subjects and controls, combined with an in-depth prospective functional study with two subjects investigating the bacterial community dynamics based on cultivation-dependent and independent methods. We observed that short-term antibiotic treatment shifted and diversified the resistome composition, increased the average copy number of antibiotic resistance genes, and altered the dominant strain genotypes in an individual-specific manner. More than 30% of the resistance genes underwent strong differentiation at the single nucleotide level during antibiotic treatment. We found that the increased potential for horizontal gene transfer, due to antibiotic administration, was [~]3-fold stronger in the differentiated resistance genes than the non-differentiated ones. This study highlights how antibiotic treatment has individualized impacts on the resistome and strain level composition, and drives the adaptive evolution of the gut microbiota.

microbiology↗

bioRxiv2018

NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning

The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unravelling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed.\n\nHere, we present an updated and extended version of the NetSurfP tool (http://www.cbs.dtu.dk/services/NetSurfP-2.0/), that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences.\n\nWe assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1,000 proteins in less than 2 hours, and complete proteomes in less than 1 day.

bioinformatics↗

bioRxiv2017

Genetic-metabolic coupling for targeted metabolic engineering

To produce chemicals, microbes typically employ potent biosynthetic enzymes that interact with native metabolism to affect cell fitness as well as chemical production. However, production optimization largely relies on data collected from wild type strains in the absence of metabolic perturbations, thus limiting their relevance to specific process scenarios. Here, we address this issue by coupling cell fitness to the production of thiamine diphosphate in Escherichia coli using a synthetic RNA biosensor.\n\nWe apply this system to interrogate a library of transposon mutants to elucidate the native gene network influencing both cell fitness and thiamine production. Specifically, we identify uncharacterized effectors of the OxyR-SoxR stress response that limit thiamine biosynthesis via alternative regulation of iron storage and Fe-S-cluster inclusion in enzymes.\n\nOur study represents a new generalizable approach for the reliable high-throughput identification of genetic targets of both known and unknown function that are directly relevant to a specific biosynthetic process.

bioengineering↗

bioRxiv2017

The Resistome Of Important Human Pathogens

Genes capable of conferring resistance to clinically used antibiotics have been found in many different natural environments. However, a concise overview of the resistance genes found in common human bacterial pathogens is lacking, which complicates risk ranking of environmental reservoirs. Here, we present an analysis of potential antibiotic resistance genes in the 17 most common bacterial pathogens isolated from humans. We analyzed more than 20,000 bacterial genomes and defined a clinical resistome as the set of resistance genes found across these genomes. Using this database, we uncovered the co-occurrence frequencies of the resistance gene clusters within each species enabling identification of co-dissemination and co-selection patterns. The resistance genes identified in this study represent the subset of the environmental resistome that is clinically relevant and the dataset and approach provides a baseline for further investigations into the abundance of clinically relevant resistance genes across different environments. To facilitate an easy overview the data is presented at the species level at www.resistome.biosustain.dtu.dk.

microbiology↗