Whole-genome sequencing and genome-scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use

Salt tolerant organisms are increasingly being used for the industrial production of high‐value biomolecules due to their better adaptability compared to mesophiles. Chromohalobacter canadensis is one of the early halophiles to show promising biotechnology potential, which has not been explored to date. Advanced high throughput technologies such as whole‐genome sequencing allow in‐depth insight into the potential of organisms while at the frontiers of systems biology. At the same time, genome‐scale metabolic models (GEMs) enable phenotype predictions through a mechanistic representation of metabolism. Here, we sequence and analyze the genome of C. canadensis 85B, and we use it to reconstruct a GEM. We then analyze the GEM using flux balance analysis and validate it against literature data on C. canadensis. We show that C. canadensis 85B is a metabolically versatile organism with many features for stress and osmotic adaptation. Pathways to produce ectoine and polyhydroxybutyrates were also predicted. The GEM reveals the ability to grow on several carbon sources in a minimal medium and reproduce osmoadaptation phenotypes. Overall, this study reveals insights from the genome of C. canadensis 85B, providing genomic data and a draft GEM that will serve as the first steps towards a better understanding of its metabolism, for novel applications in industrial biotechnology

Molecular Insights into α-Synuclein Fibrillation:A Raman Spectroscopy and Machine Learning Approach

The aggregation of α-synuclein is crucial to the development of Lewy body diseases, including Parkinson's disease and dementia with Lewy bodies. The aggregation pathway of α-synuclein typically involves a defined sequence of nucleation, elongation, and secondary nucleation, exhibiting prion-like spreading. This study employed Raman spectroscopy and machine learning analysis, alongside complementary techniques, to characterize the biomolecular changes during the fibrillation of purified recombinant wild-type α-synuclein protein. Monomeric α-synuclein was produced, purified, and subjected to a 7-day fibrillation assay to generate preformed fibrils. Stages of α-synuclein fibrillation were analyzed using Raman spectroscopy, with aggregation confirmed through negative staining transmission electron microscopy, mass spectrometry, and light scattering analyses. A machine learning pipeline incorporating principal component analysis and uniform manifold approximation and projection was used to analyze the Raman spectral data and identify significant peaks, resulting in differentiation between sample groups. Notable spectral shifts in α-synuclein were found in various stages of aggregation. Early changes (D1) included increases in α-helical structures (1303, 1330 cm-1) and β-sheet formation (1045 cm-1), with reductions in COO- and CH2 bond regions (1406, 1445 cm-1). By D4, these structural shifts persist with additional β-sheet features. At D7, a decrease in β-sheet H-bonding (1625 cm-1) and tyrosine ring breathing (830 cm-1) indicates further structural stabilization, suggesting a shift from initial helical structures to stabilized β-sheets and aggregated fibrils. Additionally, alterations in peaks related to tyrosine, alanine, proline, and glutamic acid were identified, emphasizing the role of these amino acids in intramolecular interactions during the transition from α-helical to β-sheet conformational states in α-synuclein fibrillation. This approach offers insight into α-synuclein aggregation, enhancing the understanding of its role in Lewy body disease pathophysiology and potential diagnostic relevance.</p

FROG analysis ensures the reproducibility of genome scale metabolic models

Genome scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses including Flux variability, Reaction deletion, Objective function, and Gene deletion to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40\% reproduced without intervention, 28\% requiring minor adjustments, and 32\% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.Competing Interest StatementThe authors have declared no competing interest.info:eu-repo/semantics/publishedVersio

Reference database

&lt;p&gt;This repository aims to gather different databases used as reference for the genome-scale metabolic model reconstruction software tools presented in the paper: C. Tarzi, G. Zampieri, N. Sullivan, C. Angione, "&lt;strong&gt;Emerging methods for genome-scale metabolic modeling&lt;/strong&gt;"&lt;/p&gt

Emerging methods for genome-scale metabolic modeling of microbial communities

Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.</p