Biology subjects

Patz, S.

Publications and source records attributed to Patz, S..

7 recordsLinked to original sources

bioRxiv2025

Amyloid beta aggregation promoted by iron leads to neuronal loss in an ex vivo model of Alzheimer's disease

Alzheimers disease (AD) is a progressive neurodegenerative disorder characterized by amyloid beta (A{beta}) plaques and neurofibrillary tangles. Despite well-established iron accumulation in the AD brain, its role in exacerbating A{beta} toxicity is often overlooked in therapeutic research. We developed a 3D ex vivo organotypic brain slice cultures (OBSC) with A{beta} monomers and ferric citrate to mimic A{beta} deposits and iron overload to investigate the impact of excess iron on A{beta} toxicity in pig and human brains. Light and electron microscopy, biochemical assays, and multiple regression modeling were employed to assess iron-mediated A{beta} toxicity in neurons and glial cells. We show that OBSC offer a close approximation of in vivo morphological and physiological properties and can retain both neurons and glial cells for extended periods, and respond to experimental manipulations. We show that iron promotes A{beta} fibrillization into long fibrils, with this process further influenced by temperature. A{beta} selectively accumulated in neurons, leading to their death, sparing glial cells. In contrast, Iron, though generally toxic to neurons, exhibited unspecific cytotoxicity. Notably, the combined presence of A{beta} and iron synergistically increased neuronal death while reducing glial cell loss. Correlation analysis revealed that this synergic interaction enhances the toxicity of each other in a mutual fashion - A{beta} directs the neuronal toxicity while iron promotes A{beta} fibrillization, leading to targeted neuronal loss. In conclusion, our findings emphasize the critical role of excess iron and A{beta} in driving neuronal death in AD, underlining the importance of targeting iron accumulation along with A{beta} clearance but also addressing in future AD therapies, while also supporting our OBSC model as a valuable platform for studying the same.

neuroscience↗

bioRxiv2024

The gases H2 and O2 in open-culture reactors influence the performance and microbiota of chain elongation into n-caproate and n-caprylate.

Medium-chain carboxylates are used in various industrial applications. These chemicals are typically extracted from palm oil, which is deemed not sustainable. Recent research has focused on microbial chain elongation using reactors to produce medium-chain carboxylates, such as n-caproate (C6) and n-caprylate (C8), from organic substrates such as wastes. Even though the production of n-caproate is relatively well-characterized, bacteria and metabolic pathways that are responsible for n-caprylate production are not. Here, three 5-L reactors with continuous membrane-based liquid-liquid extraction (i.e., pertraction) were fed ethanol and acetate and operated for an operating period of 234 days with different operating conditions. Metagenomic and metaproteomic analyses were employed. n-Caprylate production rates and reactor microbiomes differed between reactors even when operated similarly due to differences in H2 and O2 between the reactors. The complete reverse {beta}-oxidation pathway was present and expressed by several bacterial species in the Clostridia class. Several Oscillibacter spp., including Oscillibacter valericigenes, were positively correlated with n-caprylate production rates, while Clostridium kluyveri was positively correlated with n-caproate production. Pseudoclavibacter caeni, which is a strictly aerobic bacterium, was abundant across all the operating periods, regardless of n-caprylate production rates. This study provides insight into microbiota that are associated with n-caprylate production in open-culture reactors and provides ideas for further work. ImportanceMicrobial chain elongation pathways in open-culture biotechnology systems can be utilized to convert organic waste and industrial side streams into valuable industrial chemicals. Here, we investigated the microbiota and metabolic pathways that produce medium-chain carboxylates, including n-caproate (C6) and n-caprylate (C8), in reactors with in-line product extraction. Although the reactors in this study were operated similarly, different microbial communities dominated and were responsible for chain elongation. We found that different microbiota were responsible for n-caproate or n-caprylate production, and this can inform engineers on how to operate the systems better. We also observed which changes in operating conditions steered the production toward and away from n-caprylate, but more work is necessary to ascertain a mechanistic understanding that could be predictive. This study provides pertinent research questions for future work.

microbiology↗

bioRxiv2024

mgPGPT: Metagenomic analysis of plant growth-promoting traits

In a recent publication, we introduce the PGPT ontology and PGPT-db of bacterial plant growth-promotion traits and associated database of protein sequences, and provide several tools for bacterial genome analysis on the PLaBAse server. Here, we extend the scope of the PGPT ontology to perform PGPT analysis of metagenomic datasets. First, we introduce mgPGPT-db, an extended database of 39, 582, 183 protein sequences obtained computationally by including proteins from AnnoTree. With this, we have integrated the PGPT ontology into our metagenome analysis tool MEGAN and provide mapping files to identify PGPT-related genes using the results of a DIAMOND alignment of reads against either the new mgPGPT-db database, the NCBI-nr protein database, or the AnnoTree protein database. We demonstrate and compare these different approaches in detail on an example data set and evince the improvement compared to the PGPT-db. We also compare the inferred PGPT content of several samples taken from different environments and reveal plant specific PGPT clustering. IMPORTANCEA deeper understanding of plant growth-promoting traits of bacteria is important to enlight and enhance the native plant-beneficial bacterial functional diversity regarding the environmental stress adaption or the ability to suppress even food-borne pathogens by strain inoculation dedicated to agriculture and other plant production systems. The work presented here extends recent work beyond the analysis of individual to allow the assessment of the PGPT potential of metagenomes obtained from environmental samples.

bioinformatics↗

bioRxiv2023

Interpretable Machine Learning Decodes Soil Microbiome's Response to Drought Stress

BackgroundExtreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil metagenome and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable Machine Learning holds immense potential for drought stress classification in the soil metagenome based on marker taxa. ResultsThis study demonstrates that the metagenomic approach of Differential Abundance Analysis methods and Machine Learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil metagenome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested. ConclusionsIn the detection of drought stress in the soil metagenome, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

bioinformatics↗

bioRxiv2022

Two new Rhizobiales species isolated from root nodules of common sainfoin (Onobrychis viciifolia) show different plant colonization strategies

Root nodules of legume plants are primarily inhabited by rhizobial nitrogen-fixing bacteria. Here we propose two new Rhizobiales species isolated from root nodules of common sainfoin (Onobrychis viciifolia), as shown by core-gene phylogeny, overall genome relatedness indices and pan-genome analysis. Mesorhizobium onobrychidis sp. nov., actively induces nodules, and achieves atmospheric nitrogen and carbon dioxide fixation. This species appears to be depleted in motility genes, and is enriched in genes for direct effects on plant growth performance. Its genome reveals functional and plant growth-promoting signatures like a large unique chromosomal genomic island with high density of symbiotic genetic traits. Onobrychidicola muellerharveyae gen. nov. sp. nov., is described as type species of the new genus Onobrychidicola in Rhizobiaceae. This species comprises unique genetic features and plant growth-promoting traits (PGPTs), which strongly indicate its function in biotic stress reduction and motility. We applied a newly developed bioinformatics approach for in silico prediction of PGPTs (PGPT-Pred), which supports the different lifestyles of the two new species and the plant growth-promoting performance of M. onobrychidis in the greenhouse trial.

microbiology↗

bioRxiv2021

PLaBAse: A comprehensive web resource for analyzing the plant growth-promoting potential of plant-associated bacteria

Plant-beneficial microorganisms are gaining importance for sustainable plant production and phytosanitary practices. Yet there is a lack of computational approaches targeting bacterial traits associated with plant growth-promotion (PGP), which hinders the in-silico identification, comparison, and selection of phytostimulatory bacterial strains. To address this problem, we have developed the new web resource PLaBAse (v1.01, http://plabase.informatik.uni-tuebingen.de/pb/plabase.php), which provides a number of services, including (i) a database for screening 5,565 plant-associated bacteria (PLaBA-db), (ii) a tool for predicting plant growth-promoting traits (PGPTs) of single bacterial genomes (PGPT-Pred), and (iii) a tool for the prediction of bacterial plant-association by marker gene identification (PIFAR-Pred). The latter was developed by Martinez-Garcia et al. and is now hosted at University of Tuebingen. The PGPT-Pred tool is based on our new PGPT ontology, a literature- and OMICs-curated, comprehensive, and hierarchical collection of [~]6,900 PGPTs that are associated with 6,965,955 protein sequences. To study the distribution of the PGPTs across different environments, we applied it to 70,540 bacterial strains associated with (i) seven different environments (including plants), (iii) five different plant spheres (organs), and (iii) two bacteria-induced plant phenotypes. This analysis revealed that plant-symbiotic bacteria generally have a larger genome size and a higher count of PGPT-annotated protein encoding genes. Obviously, not all reported PGPTs are restricted to -or only enriched in-plant-associated and plant symbiotic bacteria. Some also occur in human- and animal-associated bacteria, perhaps due to the transmission of PGP bacteria (PGPBs) between environments, or because some functions are involved in adaption processes to various environments. Here we provide an easy-to-use approach for screening of PGPTs in bacterial genomes across various phyla and isolation sites, using PLaBA-db, and for standardized annotation, using PGPT-Pred. We believe that this resource will improve our understanding about the entire PGP processes and facilitate the prediction of PGPB as bio-inoculants and for biosafety strategies, so as to help to establish sustainable and targeted bacteria-incorporated plant production systems in the future.

bioinformatics↗

bioRxiv2019

Anti-consensus: detecting trees that have an evolutionary signal that is lost in consensus

AO_SCPLOWBSTRACTC_SCPLOWIn phylogenetics, a set of gene trees is often summarized by a consensus tree, such as the majority consensus, which is based on the set of all splits that are present in more than 50% of the input trees. A \"consensus network\" is obtained by lowering the threshold and considering all splits that are contained in 10% of the trees, say, and then computing the corresponding splits network. By construction and in practice, a consensus network usually shows the majority tree, extended by a number of rectangles that represent local rearrangements around internal nodes of the consensus tree. This may lead to the false conclusion that the input trees do not differ in a significant way because \"even a phylogenetic network\" does not display any large discrepancies. To harness the full potential of a phylogenetic network, we introduce the new concept of an anti-consensus network that aims at representing the largest interesting discrepancies found in a set of gene trees. We provide an efficient algorithm for computing an anti-consensus and illustrate its application using a set of gene trees from species and strains in the genus Kosakonia.

bioinformatics↗