Tailoring Synthetic Polypeptide Design for Directed Fibril Superstructure Formation and Enhanced Hydrogel Properties.

The biological significance of self-assembled protein filament networks and their unique mechanical properties have sparked interest in the development of synthetic filament networks that mimic these attributes. Building on the recent advancement of autoaccelerated ring-opening polymerization of amino acid N-carboxyanhydrides (NCAs), this study strategically explores a series of random copolymers comprising multiple amino acids, aiming to elucidate the core principles governing gelation pathways of these purpose-designed copolypeptides. Utilizing glutamate (Glu) as the primary component of copolypeptides, two targeted pathways were pursued: first, achieving a fast fibrillation rate with lower interaction potential using serine (Ser) as a comonomer, facilitating the creation of homogeneous fibril networks; and second, creating more rigid networks of fibril clusters by incorporating alanine (Ala) and valine (Val) as comonomers. The selection of amino acids played a pivotal role in steering both the morphology of fibril superstructures and their assembly kinetics, subsequently determining their potential to form sample-spanning networks. Importantly, the viscoelastic properties of the resulting supramolecular hydrogels can be tailored according to the specific copolypeptide composition through modulations in filament densities and lengths. The findings enhance our understanding of directed self-assembly in high molecular weight synthetic copolypeptides, offering valuable insights for the development of synthetic fibrous networks and biomimetic supramolecular materials with custom-designed properties.

Fast Targeted Metabolomics for Analyzing Metabolic Diversity of Bacterial Indole Derivatives in ME/CFS Gut Microbiome.

Disruptions in microbial metabolite interactions due to gut microbiome dysbiosis and metabolomic shifts may contribute to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and other immune-related conditions. The aryl hydrocarbon receptor (AhR), activated upon binding various tryptophan metabolites, modulates host immune responses. This study investigates whether the metabolic diversity-the concentration distribution-of bacterial indole pathway metabolites can differentiate bacterial strains and classify ME/CFS samples. A fast targeted liquid chromatography-parallel reaction monitoring method at a rate of 4 minutes per sample was developed for large-scale analysis. This method revealed significant metabolic differences in indole derivatives among B. uniformis strains cultured from human isolates. Principal component analysis identified two major components (PC1, 68.9%; PC2, 18.7%), accounting for 87.6% of the variance and distinguishing two distinct B. uniformis clusters. The metabolic difference between clusters was particularly evident in the relative contributions of indole-3-acrylate and indole-3-aldehyde. We further measured concentration distributions of indole derivatives in ME/CFS by analyzing fecal samples from 10 patients and 10 healthy controls using the fast targeted metabolomics method. An AdaBoost-LOOCV model achieved moderate classification success with a mean LOOCV accuracy of 0.65 (Control: precision of 0.67, recall of 0.60, F1-score of 0.63; ME/CFS: precision of 0.64, recall of 0.7000, F1-score of 0.67). These results suggest that the metabolic diversity of indole derivatives from tryptophan degradation, facilitated by the fast targeted metabolomics and machine learning, is a potential biomarker for differentiating bacterial strains and classifying ME/CFS samples. Mass spectrometry datasets are accessible at the National Metabolomics Data Repository (ST002308, DOI: 10.21228/M8G13Q; ST003344, DOI: 10.21228/M8RJ9N; ST003346, DOI: 10.21228/M8RJ9N).

Bioassay-Driven, Fractionation-Empowered, Focused Metabolomics for Discovering Bacterial Activators of Aryl Hydrocarbon Receptor.

Aryl hydrocarbon receptor (AhR) is a transcription factor that regulates gene expression upon ligand activation, enabling microbiota-dependent induction, training, and function of the host immune system. A spectrum of metabolites, encompassing indole and tryptophan derivatives, have been recognized as activators. This work introduces an integrated, mass spectrometry-centric workflow that employs a bioassay-guided, fractionation-based methodology for the identification of AhR activators derived from human bacterial isolates. By leveraging the workflow efficiency, the complexities inherent in metabolomics profiling are significantly reduced, paving the way for an in-depth and focused mass spectrometry analysis of bioactive fractions isolated from bacterial culture supernatants. Validation of AhR activator candidates used multiple criteria─MS/MS of the synthetic reference compound, bioassay of AhR activity, and elution time confirmation using a C-13 isotopic reference─and was demonstrated for N-formylkynurenine (NFK). The workflow reported provides a roadmap update for improved efficiency of identifying bioactive metabolites using mass spectrometry-based metabolomics. Mass spectrometry datasets are accessible at the National Metabolomics Data Repository (PR001479, Project DOI: 10.21228/M8JM7Q).

Investigation on the ability of heteroatom-doped graphene for biorecognition.

Doped graphene platforms have been attracting considerable attention due to their improved electrochemical performances. Recent studies have shown the advantage of using either p-type or n-type doped graphene materials as transducers for the detection of various electroactive probes. Here we wanted to take a step forward and extend the study to investigate the ability of heteroatom doped graphene as an electrochemical platform for biorecognition. To this aim, a boron-doped graphene, a nitrogen-doped graphene and an undoped graphene material prepared under similar conditions were employed for the detection of fumonisin B1, a highly carcinogenic mycotoxin found in food commodities. We found that the material structural features, such as the amount of oxygen functionalities, had a stronger influence on the sensitivity of biorecognition rather than the kind and amount of dopant. Our findings may be essential for the choice of a proper platform for the assessment of food safety.

Doped Graphene for DNA Analysis: the Electrochemical Signal is Strongly Influenced by the Kind of Dopant and the Nucleobase Structure.

Doping graphene with heteroatoms can alter the electronic and electrochemical properties of the starting material. Contrasting properties should be expected when the doping is carried out with electron donating species (n-type dopants) or with electron withdrawing species (p-type dopants). This in turn can have a profound influence on the electroanalytical performance of the doped material being used for the detection of specific probes. Here we investigate the electrochemical oxidation of DNA bases adenine, guanine, thymine and cytosine on two heteroatom-doped graphene platforms namely boron-doped graphene (p-type dopant) and nitrogen-doped graphene (n-type dopant). We found that overall, boron-doped graphene provided the best response in terms of electrochemical signal sensitivity for all bases. This is due to the electron deficiency of boron-doped graphene, which can promote the oxidation of DNA bases, as opposed to nitrogen-doped graphene which possesses an excess of electrons. Moreover, also the structure of the nucleobase was found to have significant influence on the obtained signal. Our study may open new frontiers in the electrochemical detection of DNA bases which is the first step for label-free DNA analysis.