Galleria mellonella is a lepidopteran whose larval stage has shown the ability to degrade polystyrene (PS), one of the most recalcitrant plastics to biodegradation. In the present study, we fed G. mellonella larvae with PS for 54 days and determined candidate enzymes for its degradation. We first confirmed the biodegradation of PS by Fourier transform infrared spectroscopy- Attenuated total reflectance (FTIR-ATR) and then identified candidate enzymes in the larval gut by proteomic analysis using liquid chromatography with tandem mass spectrometry (LC-MS/MS). Two of these proteins have structural similarities to the styrene-degrading enzymes described so far. In addition, potential hydrolases, isomerases, dehydrogenases, and oxidases were identified that show little similarity to the bacterial enzymes that degrade styrene. However, their response to a diet based solely on polystyrene makes them interesting candidates as a potential new group of polystyrene-metabolizing enzymes in eukaryotes.

Background: Determining the postmortem interval (PMI) accurately remains a significant challenge in forensic sciences, especially for intervals greater than 5 years (late PMI). Traditional methods often fail due to the extensive degradation of soft tissues, necessitating reliance on bone material examinations. The precision in estimating PMIs diminishes with time, particularly for intervals between 1 and 5 years, dropping to about 50% accuracy. This study aims to address this issue by identifying key protein biomarkers through proteomics and machine learning, ultimately enhancing the accuracy of PMI estimation for intervals exceeding 15 years. Methods: Proteomic analysis was conducted using LC–MS/MS on skeletal remains, specifically focusing on the tibia and ribs. Protein identification was performed using two strategies: a tryptic-specific search and a semitryptic search, the latter being particularly beneficial in cases of natural protein degradation. The Random Forest algorithm was used to model protein abundance data, enabling the prediction of PMI. A thorough screening process, combining importance scores and SHAP values, was employed to identify the most informative proteins for model’s training and accuracy. Results: A minimal set of three biomarkers—K1C13, PGS1, and CO3A1—was identified, significantly improving the prediction accuracy between PMIs of 15 and 20 years. The model, based on protein abundance data from semitryptic peptides in tibia samples, achieved sustained 100% accuracy across 100 iterations. In contrast, non-supervised methods like PCA and MCA did not yield comparable results. Additionally, the use of semitryptic peptides outperformed tryptic peptides, particularly in tibia proteomes, suggesting their potential reliability in late PMI prediction. Conclusions: Despite limitations such as sample size and PMI range, this study demonstrates the feasibility of combining proteomics and machine learning for accurate late PMI predictions. Future research should focus on broader PMI ranges and various bone types to further refine and standardize forensic proteomic methodologies for PMI estimation.

Agmatine is a neurotransmitter with anticonvulsant, anti-neurotoxic and antidepressant-like effects, in addition it has hypoglycemic actions. Agmatine is converted to putrescine and urea by agmatinase (AGM) and by an agmatinase-like protein (ALP), a new type of enzyme which is present in human and rodent brain tissues. Recombinant rat brain ALP is the only mammalian protein that exhibits significant agmatinase activity in vitro and generates putrescine under in vivo conditions. ALP, despite differing in amino acid sequence from all members of the ureohydrolase family, is strictly dependent on Mn²⁺ for catalytic activity. However, the Mn²⁺ ligands have not yet been identified due to the lack of structural information coupled with the low sequence identity that ALPs display with known ureohydrolases. In this work, we generated a structural model of the Mn²⁺ binding site of the ALP and we propose new putative Mn²⁺ ligands. Then, we cloned and expressed a sequence of 210 amino acids, here called the “central-ALP”, which include the putative ligands of Mn²⁺. The results suggest that the central-ALP is catalytically active, as agmatinase, with an unaltered K_m for agmatine and a decreased k_cat. Similar to wild-type ALP, central-ALP is activated by Mn²⁺ with a similar affinity. Besides, a simple mutant D217A, a double mutant E288A/K290A, and a triple mutant N213A/Q215A/D217A of these putative Mn²⁺ ligands result on the loss of ALP agmatinase activity. Our results indicate that the central-ALP contains the active site for agmatine hydrolysis, as well as that the residues identified are relevant for the ALP catalysis.

Biofilm formation, as well as the glycation of collagen associated with aging and hyperglycaemia, can modulate Streptococcus mutans bacterial extracellular vesicle characteristics and cargo.

Background: The advancement of hybrid sequencing technologies is increasingly expanding genome assemblies that are often annotated using hybrid sequencing transcriptomics, leading to improved genome characterization and the identification of novel genes and isoforms in a wide variety of organisms. Results: We developed an easy-to-use genome-guided transcriptome annotation pipeline that uses assembled transcripts from hybrid sequencing data as input and distinguishes between coding and long non-coding RNAs by integration of several bioinformatic approaches, including gene reconciliation with previous annotations in GTF format. We demonstrated the efficiency of this approach by correctly assembling and annotating all exons from the chicken SCO-spondin gene (containing more than 105 exons), including the identification of missing genes in the chicken reference annotations by homology assignments. Conclusions: Our method helps to improve the current transcriptome annotation of the chicken brain. Our pipeline, implemented on Anaconda/Nextflow and Docker is an easy-to-use package that can be applied to a broad range of species, tissues, and research areas helping to improve and reconcile current annotations. The code and datasets are publicly available at https://github.com/cfarkas/annotate_my_genomes

The establishment and maintenance of nucleosome-free regions (NFRs) are prominent processes within chromatin dynamics. Transcription factors, ATP-dependent chromatin remodeling complexes (CRCs) and DNA sequences are the main factors involved. In Saccharomyces cerevisiae, CRCs such as RSC contribute to chromatin opening at NFRs, while other complexes, including ISW1a, contribute to NFR shrinking. Regarding DNA sequences, growing evidence points to poly(dA:dT) tracts as playing a direct role in active processes involved in nucleosome positioning dynamics. Intriguingly, poly(dA:dT)-tract-containing NFRs span asymmetrically relative to the location of the tract by a currently unknown mechanism. In order to obtain insight into the role of poly(dA:dT) tracts in nucleosome remodeling, we performed a systematic analysis of their influence on the activity of ISW1a and RSC complexes. Our results show that poly(dA:dT) tracts differentially affect the activity of these CRCs. Moreover, we found differences between the effects exerted by the two alternative tract orientations. Remarkably, tract-containing linker DNA is taken as exit DNA for nucleosome sliding catalyzed by RSC. Our findings show that defined DNA sequences, when present in linker DNA, can dictate in which direction a remodeling complex has to slide nucleosomes and shed light into the mechanisms underlying asymmetrical chromatin opening around poly(dA:dT) tracts.