Biomedical subjects

Michael Seeger

Publications and source records attributed to Michael Seeger.

4 recordsLinked to original sources

PubMed2026

Biotic and abiotic degradation of PHAs: mechanisms, environments, and potential applications of degradation products.

This review seeks to compile Polyhydroxyalkanoates (PHAs) degradation studies published over the past 20 years. It highlights the effect of physical properties, such as crystallinity and molecular weight, on the decomposition rate of these molecules. Both biotic processes, mediated by bacteria, fungi, and enzymes, as well as abiotic processes, such as hydrolysis and thermal degradation, are analyzed. A repertoire of diverse microorganisms, including their metabolic pathways and enzymes for PHA breakdown, is presented. Furthermore, this review presents the decomposition of PHAs in various environments, such as soil and seawater, highlighting their potential as a sustainable alternative. Finally, the resulting degradation products are described, emphasizing their potential applications in medicine and industry. Although degradation of PHAs has been extensively studied through these years, several knowledge gaps remain undisclosed, including the degradation of diverse polyester monomers. PHAs comprise numerous monomer compositions with variable properties, which present opportunities for different applications but pose a challenge in their degradation. The reader of this review can extract useful information for both the production of PHAs and their potential applications.

Biodegradation↗

PubMed2025

Versatile sugar and valerate metabolic pathways in Paraburkholderia xenovorans LB400 enable tailored poly(3-hydroxybutyrate-co-3-hydroxyvalerate) production.

Poly(3-hydroxybutyrate) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) polymers are accumulated by diverse prokaryotes. Their distinct monomer compositions enable their use as tailored bioplastics. The aims were to characterize the poly(3-hydroxybutyrate) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) synthesis by Paraburkholderia xenovorans LB400 using different sugars and valerate, and to gain genome-oriented insights into polyhydroxyalkanoate production. d-Glucose, d-mannitol, d-gluconate, and d-xylose were evaluated as sole carbon sources or supplemented with valerate. Polyhydroxyalkanoates synthesized by strain LB400 were characterized through GC-MS, GC-FID, FTIR, and 1H and 13C-NMR. P. xenovorans LB400 reached 1.00-1.39 g L-1 of dry cell weight (DCW) with a P(3HB) content of 21-43% w w-1 when grown on different sugars. The addition of valerate to the sugar-grown LB400 cultures yielded a DCW of 1.79 to 2.29 g L-1 and a P(3HB-co-3HV) content of 50.0‒51.2% w w-1, with varying 3HV compositions (28‒43 mol%). The highest 3HV incorporation was observed with d-xylose and valerate. Genomic analyses of strain LB400 revealed key elements of sugar metabolism influencing growth, polymer accumulation, and monomer composition. LB400 genome encodes the PhaJ-like R-specific hydratase and FadJ epimerase, which are potentially useful for modulating copolymer composition. PHA production under bioreactor conditions was evaluated. In a bioreactor fed with d-glucose, LB400 achieved a P(3HB) concentration of 2.2 g L-1. These findings highlight the metabolic versatility of P. xenovorans LB400 in utilizing diverse sugars to produce either P(3HB) or tailor-made P(3HB-co-3HV), supporting the development of bioplastics for specific applications. KEY POINTS: • Strain LB400 produced P(3HB-co-3HV) from various sugars and valerate. • Sugar type drives LB400 PHA copolymer synthesis and composition. • Strain LB400 PHA production was scaled up to a bioreactor.

Polyesters↗

PubMed2025

Negative dataset selection impacts machine learning-based predictors for multiple bacterial species promoters.

MOTIVATION: Advances in bacterial promoter predictors based on machine learning have greatly improved identification metrics. However, existing models overlooked the impact of negative datasets, previously identified in GC-content discrepancies between positive and negative datasets in single-species models. This study aims to investigate whether multiple-species models for promoter classification are inherently biased due to the selection criteria of negative datasets. We further explore whether the generation of synthetic random sequences (SRS) that mimic GC-content distribution of promoters can partly reduce this bias. RESULTS: Multiple-species predictors exhibited GC-content bias when using CDS as a negative dataset, suggested by specificity and sensibility metrics in a species-specific manner, and investigated by dimensionality reduction. We demonstrated a reduction in this bias by using the SRS dataset, with less detection of background noise in real genomic data. In both scenarios DNABERT showed the best metrics. These findings suggest that GC-balanced datasets can enhance the generalizability of promoter predictors across Bacteria. AVAILABILITY AND IMPLEMENTATION: The source code of the experiments is freely available at https://github.com/maigonzalezh/MultispeciesPromoterClassifier.

Machine Learning↗

PubMed2025

Surfing in the storm: how Paraburkholderia xenovorans thrives under stress during biodegradation of toxic aromatic compounds and other stressors.

The adaptive mechanisms of Burkholderiales during the catabolism of aromatic compounds and abiotic stress are crucial for their fitness and performance. The aims of this report are to review the bacterial adaptation mechanisms to aromatic compounds, oxidative stress, and environmental stressful conditions, focusing on the model aromatic-degrading Paraburkholderia xenovorans LB400, other Burkholderiales, and relevant degrading bacteria. These mechanisms include (i) the stress response during aromatic degradation, (ii) the oxidative stress response to aromatic compounds, (iii) the metabolic adaptation to oxidative stress, (iv) the osmoadaptation to saline stress, (v) the synthesis of siderophore during iron limitation, (vi) the proteostasis network, which plays a crucial role in cellular function maintenance, and (vii) the modification of cellular membranes, morphology, and bacterial lifestyle. Remarkably, we include, for the first time, novel genomic analyses on proteostasis networks, carbon metabolism modulation, and the synthesis of stress-related molecules in P. xenovorans. We analyzed these metabolic features in silico to gain insights into the adaptive strategies of P. xenovorans to challenging environmental conditions. Understanding how to enhance bacterial stress responses can lead to the selection of more robust strains capable of thriving in polluted environments, which is critical for improving biodegradation and bioremediation strategies.

Biodegradation, Environmental↗