Biology subjects

Huson, D.

Publications and source records attributed to Huson, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Development of a continuous bioreactor to maintain stable nasal microbiomes from swab specimens and synthetic communities

BackgroundThe nasal microbiome is a collection of diverse microbial populations that inhabit the nose. Staphylococcus aureus is the most common opportunistic pathogen that colonizes the nasal mucosa, increasing the risk of invasive infections in immunocompromised and hospitalized patients. Clinicians usually prescribe antibiotics to decolonize the nasal cavities of at-risk patients from S. aureus. However, their broad antimicrobial activity can damage the resident nasal microbiome. Instead, naturally occurring compounds or resident bacteria in nasal microbiomes can effectively and safely exclude S. aureus from the nose. Cell culture and animal models have been used for nasal microbiome studies. However, their unstable microbiomes reduce the accuracy and reliability of the results. Recently, continuous bioreactors have been proposed as alternatives to these models. ResultsWe designed and operated a continuous bioreactor system to maintain stable nasal microbiomes. Next, we inoculated the bioreactor with nasal-swab specimens that we had collected from healthy volunteers. We operated the bioreactors under varying conditions (i.e., operating mode, dilution rate, temperature, pH, and medium composition), and determined the optimal conditions (continuous mode, 1 d-1, 30xlink:href=" pH 6.5, and synthetic nasal medium 3), resulting in stable microbiomes consisting of the main nasal bacterial species. The nasal microbiomes in the optimized bioreactors showed high reproducibility and resilience during a pH perturbation. Moreover, all microbiomes in the bioreactor, which were inoculated with six different nasal-swab specimens, maintained stable bacterial and metabolite compositions. In addition, we applied a synthetic microbial community (SynCom), which was derived from one of the volunteers, to demonstrate a S. aureus decolonization strategy. The bioreactor, inoculated with this SynCom, maintained a stable nasal microbiome for more than one month. Finally, different S. aureus strains that we inoculated in the SynCom showed distinct growth patterns within the otherwise stable community. ConclusionsThe continuous bioreactor enables the cultivation of stable nasal microbiomes for longer than one month by mimicking the environmental conditions of the human nose. The bioreactor is a valuable model for understanding the functions of the nasal microbiome and devising new decolonization strategies against S. aureus.

bioengineering↗

bioRxiv2025

Displacement-Optimized Tanglegrams for Trees and Networks

Phylogenetic trees and networks play a central role in biology, bioinformatics, and mathematical biology, and producing clear, informative visualizations of them is an important task. Tanglegrams, which display two phylogenies side by side with lines connecting shared taxa, are widely used for comparing evolutionary histories, host-parasite associations, and horizontal gene transfer. Existing layout algorithms have largely focused on trees and on minimizing the number of inter-taxon edge crossings. We introduce displacement-optimized tanglegrams (DO-tanglegrams), a new approach that applies equally to trees and rooted phylogenetic networks. Our method explicitly minimizes taxon displacement - the vertical misalignment of corresponding taxa across the two sides - and reticulate displacement - the vertical distance spanned by reticulation edges within a network. We formalize one-sided and two-sided optimization problems, show that exact minimization is computationally intractable, and propose a heuristic that combines exhaustive local search with simulated annealing. The algorithm naturally accommodates unresolved nodes (multifurcations or multicombinations) and missing taxa. We have implemented the DO-tanglegram algorithm in SplitsTree. We compare our implementation against the cophylo R-package on a collection of synthetic trees, and against the NN-tanglegram algorithm in Dendroscope on a collection of synthetic networks. The results indicate that DO-tanglegram performs significantly better than cophylo on trees and than NN-tanglegram on networks. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/690634v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@13c9d71org.highwire.dtl.DTLVardef@849fcborg.highwire.dtl.DTLVardef@116ea8borg.highwire.dtl.DTLVardef@1805470_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Sketch, capture and layout phylogenies

Phylogenetic trees and networks play a central role in biology, bioinformatics, and mathematical biology, and producing clear, informative visualizations of them is an important task. We present new algorithms for visualizing rooted phylogenetic networks in either a "combining" or "transfer" view, in both cladogram and phylogram style. In addition, we introduce a layout algorithm that aims to improve clarity by minimizing the total reticulate displacement of reticulate edges. To address the common issue that biological publications often omit machine-readable representations of depicted trees and networks, we also provide an image-based algorithm that assists in extracting their topology from figures. All algorithms are implemented in our new PhyloSketch app, which is open source and freely available at: https://github.com/husonlab/phylosketch2. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/646633v3_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@15113f0org.highwire.dtl.DTLVardef@1f41b45org.highwire.dtl.DTLVardef@12a606dorg.highwire.dtl.DTLVardef@120a51d_HPS_FORMAT_FIGEXP M_FIG C_FIG Author SummaryPhylogenetic trees and networks provide visual representations of evolutionary relationships, but creating clear and accurate diagrams can be challenging, especially when evolutionary histories involve hybridization, recombination, or horizontal gene transfer. We present PhyloSketch, an interactive app for sketching, capturing, and improving the layout of phylogenetic trees and networks. The program introduces visualization algorithms that display evolutionary relationships in alternative "combining" and "transfer" views, and in both cladogram and phylogram styles. A novel layout algorithm enhances clarity by minimizing the displacement of reticulate edges--connections that represent non-tree-like evolution. To help make published results more reproducible, PhyloSketch also includes an image-based tool that assists users in capturing network structures from figures. Together, these features make it easier to produce, analyze, and share high-quality visualizations of evolutionary relationships in research and education. PhyloSketch is open source and freely available at https://github.com/husonlab/phylosketch2.

bioinformatics↗

bioRxiv2024

Toward industrial C8 production: Oxygen intrusion drives renewable n caprylate production from ethanol and acetate via intermediate metabolite production

Previous bioreactor studies achieved high volumetric n-caprylate (i.e., n-octanoate) production rates and selectivities from ethanol and acetate with chain-elongating microbiomes. However, the metabolic pathways from the substrates to n-caprylate synthesis were unclear. We operated two n-caprylate-producing upflow bioreactors with a synthetic medium to study the underlying metabolic pathways. The operating period exceeded 2.5 years, with a peak volumetric n-caprylate production rate of 190 {+/-} 8.4 mmol C L-1 d-1 (0.14 g L-1 h-1). We identified oxygen availability as a critical performance parameter, facilitating intermediate metabolite production from ethanol. Bottle experiments in the presence and absence of oxygen with 13C-labeled ethanol suggest acetyl-coenzyme A-based derived production of n-butyrate (i.e., n-butanoate), n-caproate (i.e., n-hexanoate), and n-caprylate. Here, we postulate a trophic hierarchy within the bioreactor microbiomes based on metagenomics, metaproteomics, and metabolomics data, as well as experiments with a Clostridium kluyveri isolate. First, the aerobic bacterium Pseudoclavibacter caeni and the facultative anaerobic fungus Cyberlindnera jadinii converted part of the ethanol pool into the intermediate metabolites succinate, lactate, and pyroglutamate. Second, the strict anaerobic C. kluyveri elongated acetate with the residual ethanol to n-butyrate. Third, Caproicibacter fermentans and Oscillibacter valericigenes elongated n-butyrate with the intermediate metabolites to n-caproate and then to n-caprylate. Among the carbon chain-elongating pathways of carboxylates, the tricarboxylic acid cycle and the reverse {beta}-oxidation pathways showed a positive correlation with n-caprylate production. The results of this study inspire the realization of a chain-elongating production platform with separately controlled aerobic and anaerobic stages to produce n-caprylate renewably as an attractive chemical from ethanol and acetate as substrates. Broader contextNext to renewable electric energy, carbon-based chemicals have to be produced sustainably and independently from fossil sources. To meet this goal, we must expand the portfolio of bio-based conversion technologies on an industrial scale to cover as many target chemicals as possible. We explore the bioprocess of chain elongation to provide medium-chain carboxylates that can function as future platform chemicals in the circular economy. The most valuable medium-chain carboxylate produced with chain elongation is n-caprylate (i.e., n-octanoate). This molecule with eight carbon atoms in a row (C8) is challenging to produce renewably for the chemical industry. Previous reports elucidated that elevated ethanol-to-acetate ratios, which are found in syngas-fermentation effluent, stimulated n-caprylate production. Until now, studies have suggested that chain elongation from high concentrations of ethanol and acetate is a fully anaerobic process. We refine this view by showing a trophic hierarchy of aerobic and anaerobic microbes capable of facilitating this process. Appropriate oxygen supplementation enables the synthesis of succinate, lactate, and pyroglutamate that permit high-rate chain elongation to n-caprylate under anaerobic conditions. Given these results, future research should focus on the segregated study of aerobic and anaerobic microbes to further enhance the process performance to produce n-caprylate renewably at an industrial scale.

bioengineering↗

bioRxiv2024

PhyloFusion- Fast and easy fusion of rooted phylogenetic trees into rooted phylogenetic networks

Unrooted phylogenetic networks are often used to represent evolutionary data when incompatibilities are present. Although rooted phylogenetic networks are better suited for explicitly depicting evolutionary histories that involve reticulate events, they have been rarely used in practice, due to a lack of appropriate methods for their calculation. Here we present PhyloFusion, a fast and easily-applicable method for calculating rooted phylogenetic networks on sets of rooted phylogenetic trees. The algorithm can handle trees with unresolved nodes (which arise when edges with low support are contracted) and missing taxa. We illustrate how to use the algorithm to explore different groups of functionally-related genes and report that the algorithm can be applied to datasets containing tens of trees and hundreds of taxa. AvailabilityAn open source implementation of PhyloFusion is available in SplitsTree, https://www.github.com/husonlab/splitstree6 (GPLv3 license)

bioinformatics↗

bioRxiv2023

Self-generating autocatalytic networks: structural results, algorithms, and their relevance to evolutionary processes

The concept of an autocatalytic network of reactions that can form and persist, starting from just an available food source, has been formalised by the notion of a Reflexively-Autocatalytic and Food generated (RAF) set. The theory and algorithmic results concerning RAFs have been applied to a range of settings, from metabolic questions arising at the origin of life, to ecological networks, and cognitive models in cultural evolution. In this paper, we present new structural and algorithmic results concerning RAF sets, by studying more complex modes of catalysis that allow certain reactions to require multiple catalysts (or to not require catalysis at all), and discuss the differing ways catalysis has been viewed in the literature. We then focus on the structure and analysis of minimal RAFs, and derive structural results and polynomial-time algorithms, with applications to metabolic network data described briefly.

evolutionary biology↗

bioRxiv2023

MR-DNA: Flexible 5mC-Methylation-Site Recognition in DNA Sequences using Token Classification

DNA 5-methylcytosine modification has been widely studied in mammals and plays an important role in epigenetics. Several computational methods exist that attempt to determine the methylation state of a DNA sequence centered at a possible methylation site. Here, we introduce a novel deep-learning framework, MR-DNA, that predicts the methylation state of a single nucleotide located in a gene promoter region. The idea is to adapt the named-entity recognition approach to methylation-site prediction and to incorporate biological rules during model construction. MR-DNA has a stacked model architecture consisting of a pre-trained MuLan-Methyl-DistilBERT language model and a conditional random field algorithm, trained with a self-defined methyl loss function. The resulting fine-tuned model achieves an accuracy of 97.9% on an independent test dataset of samples. An advantage of this formulation of the methylation-site identification task is that it predicts on every nucleotide of a sequence of a given length, unlike previous methods that the predict methylation state of DNA sequences of a short fixed length. For training and testing purposes, we provide a database of DNA sequences containing verified 5mC-methylation sites, calculated from data for eight human cell lines downloaded from the ENCODE database.

bioinformatics↗

bioRxiv2023

Phylogenetic distance and structural diversity directing a reclassification of glycopeptide antibiotics

Glycopeptide antibiotics (GPAs) are key agents against multidrug-resistant Gram-positive pathogens, yet both the term "glycopeptide" and the current GPA type I-V classification framework have become increasingly strained as structurally and mechanistically divergent members continue to be discovered. In particular, compounds historically grouped as "type V GPAs" differ from classical GPAs in features such as glycosylation, peptide length, and reported mode of action, raising the question of whether they belong to the same natural product class. Here, a curated dataset of GPA-associated biosynthetic gene clusters (BGCs) is analysed by combining fingerprint similarity of the products with phylogenetic analysis of the BGCs. Fingerprint-based structural similarity networks and BGC similarity comparisons reveal a pronounced separation between classical lipid II-binding GPAs (types I-IV) and type V GPAs. Multi-locus phylogenetic analyses of conserved biosynthetic components further support two deeply divergent evolutionary subclasses, consistent with subclass-specific biosynthetic signatures. Together, these results motivate a revised, unambiguous framework in which the broader class is termed xyclopeptides, comprising the subclasses dalabactins (legacy GPA types I-IV) and murobactins (legacy type V).

genetics↗

bioRxiv2022

An integrated systems-biology platform for power-to-gas technology

Methanogenesis allows methanogenic archaea (methanogens) to generate cellular energy for their growth while producing methane. Hydrogenotrophic methanogens thrive on carbon dioxide and molecular hydrogen as sole carbon and energy sources. Thermophilic and hydrogenotrophic Methanothermobacter spp. have been recognized as robust biocatalysts for a circular carbon economy and are now applied in power-to-gas technology. Here, we generated the first manually curated genome-scale metabolic reconstruction for three Methanothermobacter spp.. We investigated differences in the growth performance of three wild-type strains and one genetically engineered strain in two independent chemostat bioreactor experiments. In the first experiment, with molecular hydrogen and carbon dioxide, we found the highest methane production rate for Methanothermobacter thermautotrophicus {Delta}H, while Methanothermobacter marburgensis Marburg reached the highest biomass growth rate. Systems biology investigations, including implementing a pan-model that contains combined reactions from all three microbes, allowed us to perform an interspecies comparison. This comparison enabled us to identify crucial differences in formate anabolism. In the second experiment, with sodium formate, we found stable growth with an M. thermautotrophicus {Delta}H plasmid-carrying strain with similar performance parameters compared to wild-type Methanothermobacter thermautotrophicus Z-245. Our findings reveal that formate anabolism influences the diversion of carbon to biomass and methane with implications for biotechnological applications of Methanothermobacter spp. in power-to-gas technology and for chemical production. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/522236v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1d468aeorg.highwire.dtl.DTLVardef@24a595org.highwire.dtl.DTLVardef@1f2dd60org.highwire.dtl.DTLVardef@1a3d8fc_HPS_FORMAT_FIGEXP M_FIG C_FIG Broader contextRenewable energy sources (e.g., wind and solar) provide carbon-free electric power. However, their intermittency and offset between peak production and demand generate the need to store this electric power. Furthermore, these technologies alone do not satisfy the demand for carbon-based commodities. Power-to-gas technology provides a means to store intermittent renewable electric power with concomitant carbon dioxide recycling into a chemical energy carrier, such as methane, on a centralized and decentralized scale. This is particularly important to establish equitable energy strategies for all countries, as is highlighted by the United Nations Sustainable Development Goals. With this work, we provide an integrated systems-biology platform for Methanothermobacter spp. to optimize biological power-to-gas technology and formulate strategies to produce other value-added products besides methane.

microbiology↗

bioRxiv2022

Megan Server: facilitating interactive access to metagenomic data on a server

MotivationMetagenomic projects of large sequencing datasets (totaling hundreds of gigabytes of data). Thus, computational preprocessing and analysis are usually performed on a server rather than on a personal computer. The results of such analyses are then usually explored interactively. One approach is to use MEGAN, an interactive program that allows analysis and comparison of metagenomic datasets. Previous releases of MEGAN have required that the user first download the computed data from the server, an increasingly time-consuming process. Here we present Megan Server, a stand-alone program that serves MEGAN files to the web, using a RESTful API, facilitating interactive analysis without downloading the complete data. We describe a number of different application scenarios. AvailabilityMegan Server is provided as a standalone program tools/megan-server in the Megan6 software suite, available at https://software-ab.cs.uni-tuebingen.de/download/megan6.

bioinformatics↗

bioRxiv2022

DeepToA: An Ensemble Deep-Learning Approach to Predicting the Theater of Activity of a Microbiome

MotivationMetagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a "theater of activity" (ToA). To what degree does the taxonomic and functional content of the former depend on the (details of the) latter? More technically, given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? Here we present a deep learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make it more amenable to deep learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the theater of activity" of microbial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. ResultsBased on 7,560 metagenomic profiles downloaded from MGnify, classified into ten different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.61%. We show that adding textual information to functional features increases the accuracy. AvailabilityOur approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. Contactdaniel.huson@uni-tuebingen.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Interactive Analysis of Biosurfactants in Fruit-Waste Fermentation Samples using BioSurfDB and MEGAN

Microbial biosurfactants are of major interest due to their multifunctional properties, biodegradable nature and low toxicity. Agroindustrial waste, such as fruit waste, can be used as substrates for producing bacteria. In this study, six samples of fruit waste, from oranges, mangoes and mixed fruits, were self-fermented, and then subjected to short-read metagenomic sequencing, so as to allow assessment of the potential of the associated microbiota for biosurfactant production. Taxonomic analysis using the DIAMOND+MEGAN analysis pipeline shows that all six samples are dominated by Proteobacteria, in particular, a common core consisting of the genera Klebsiella, Enterobacter, Stenotrophomonas, Acinetobacter and Escherichia. To support the interactive visualization and exploration of the surfactant-related genes in such samples, we have integrated the BiosurfDB classification into MEGAN and make this available. Functional analysis indicates high similarity among samples and that a significant number of reads map to genes that are involved in the biosynthesis of lipopeptide-class biosurfactants. Gene-centric analysis reveals Klebsiella as the main assignment for genes related to putisolvins biosynthesis. This suggests that fruit waste is a promising substrate for fermentative processes because the associated microbiota may be able to produce biosurfactants that are potentially useful for the agricultural, chemical, food and pharmaceutical industries.

bioinformatics↗

bioRxiv2021

Low-Cost, Modular Modification to a Desktop 3D Printer for General Purpose Gel/Paste Extrusion & Direct Ink Writing

We propose a design for a simple paste extruder modification that can be used for the selective deposition and patterning of gels and pastes, using a desktop 3D printer as the primary platform. This technology has found use with a variety of materials in seemingly disparate fields, including the printing of ceramics, food and biological materials, each with a variety of material-specific solutions to enhance printability. However, we focus on a syringe-pump driven system that is simple, low-cost, modular, easily assembled and highly modifiable with a low barrier of entry in order to maximise the generalisability and range of printable materials.

bioengineering↗

bioRxiv2020

The isolate Caproiciproducens sp. 7D4C2 produces n-caproate at mildly acidic conditions from hexoses: genome and rBOX comparison with related strains and chain-elongating bacteria

BackgroundBulk production of medium-chain carboxylates (MCCs) with 6-12 carbon atoms is of great interest to biotechnology. Open cultures (e.g., reactor microbiomes) have been utilized to generate MCCs in bioreactors. When in-line MCC extraction and prevention of product inhibition is required, the bioreactors have been operated at mildly acidic pH (5.0-5.5). However, model chain-elongating bacteria grow optimally at neutral pH values. Here, we isolated a chain-elongating bacterium (strain 7D4C2) that thrives at mildly acidic pH. We studied its metabolism and compared its whole genome and the reverse {beta}-oxidation (rBOX) genes to other bacteria. ResultsStrain 7D4C2 produces lactate, acetate, n-butyrate, n-caproate, biomass, and H2/CO2 from hexoses. With only fructose as substrate (pH 5.5), the maximum n-caproate specificity (i.e., products per other carboxylates produced) was 60.9 {+/-} 1.5%. However, this was considerably higher at 83.1 {+/-} 0.44% when both fructose and n-butyrate (electron acceptor) were combined as a substrate. A comparison of serum bottles with fructose and n-butyrate with an increasing pH value from 4.5 to 9.0 showed a decreasing n-caproate specificity from [~]92% at mildly acidic pH (pH 4.5-5.0) to [~]24% at alkaline pH (pH 9.0). Moreover, when carboxylates were extracted from the broth (undissociated n-caproic acid was [~]0.3 mM), the n-caproate selectivity (i.e., product per substrate fed) was 42.6 {+/-} 19.0% higher compared to serum bottles without extraction. Based on the 16S rRNA gene sequence, strain 7D4C2 is most closely related to the isolates Caproicibacter fermentans (99.5%) and Caproiciproducens galactitolivorans (94.7%), which are chain-elongating bacteria that are also capable of lactate production. Whole-genome analyses indicate that strain 7D4C2, C. fermentans, and C. galactitolivorans belong to the same genus of Caproiciproducens. Their rBOX genes are conserved and located next to each other, forming a gene cluster, which is different than for other chain-elongating bacteria such as Megasphaera spp. ConclusionsCaproiciproducens spp., comprising strain 7D4C2, C. fermentans, C. galactitolivorans, and several unclassified strains, are chain-elongating bacteria that encode a highly conserved rBOX gene cluster. Caproiciproducens sp. 7D4C2 (DSM 110548) was studied here to understand n-caproate production better at mildly acidic pH within microbiomes and has the additional potential as a pure-culture production strain to convert sugars into n-caproate.

microbiology↗

bioRxiv2020

Analysis procedures for assessing recovery of high quality, complete, closed genomes from Nanopore long read metagenome sequencing

New long read sequencing technologies offer huge potential for effective recovery of complete, closed genomes from complex microbial communities. Using long read (MinION) obtained from an ensemble of activated sludge enrichment bioreactors, we 1) describe new methods for validating long read assembled genomes using their counterpart short read metagenome assembled genomes; 2) assess the influence of different correction procedures on genome quality and predicted gene quality and 3) contribute 21 new closed or complete genomes of community members, including several species known to play key functional roles in wastewater bioprocesses: specifically microbes known to exhibit the polyphosphate- and glycogen-accumulating organism phenotypes (namely Accumulibacter and Dechloromonas, and Micropruina and Defluviicoccus, respectively), and filamentous bacteria (Thiothrix) associated with the formation and stability of activated sludge flocs. Our findings further establish the feasibility of long read metagenome-assembled genome recovery, and demonstrate the utility of parallel sampling of moderately complex enrichments communities for recovery of genomes of key functional species relevant for the study of complex wastewater treatment bioprocesses.

bioinformatics↗

bioRxiv2019

Human gut mobileome during antibiotic therapy. A trajectory-based approach to analysis of the metagenomic time-series datasets

MotivationAntibiotic resistance is widely recognized as a severe threat to current medical practice. Each antibiotic therapy drives the emergence and subsequent retention of antibiotics resistance genes within the human gut microbiome. However, the details on how the resistance spreads between bacteria within the human gut remain unknown, as does the role of horizontal gene transfer in this process, too.\n\nResultsWe present a novel approach to the analysis of time-series whole-genome metagenomic sequencing data. This involves partitioning the scaffolds from the metagenomic assembly into groups corresponding to bacterial chromosomes, plasmids and those with prophages and transposons. Using specialized sequencing of the bacteriophages we were able to track the flow of ciprofloxacin resistance genes from bacterial chromosomes, through the plasmids, to prophages and phages.\n\nContactanna.gorska@univr.it.

bioinformatics↗

bioRxiv2019

Enriched environmental conditions modify the gut microbiome composition and fecal markers of inflammation in Parkinson’s disease

BackgroundRecent findings suggest an implication of the gut microbiome in Parkinsons disease patients. Parkinsons disease onset and progression has also been linked with various environmental factors such as physical activity, exposure to pesticides, head injury, nicotine, and dietary factors.\n\nObjectivesIn this study, we used a transgenic mouse model overexpressing the complete human SNCA genes modeling familial and sporadic forms of Parkinsons disease to study whether environmental conditions such as standard versus enriched environment changes the gut microbiome and influences disease progression.\n\nMethodsWe performed 16S rRNA DNA sequencing on fecal samples for microbiome analysis and studied fecal inflammatory calprotectin from the colon of control and transgenic mice kept under standard environment and enriched environment conditions.\n\nResultsThe overall composition of the gut microbiota was not changed in transgenic mice compared with wild-type in enriched environment, however, individual gut bacteria at genus level such as Lactobacillus sp. were significantly changed in transgenic mice. Furthermore, enriched environment significantly reduced colon fecal inflammatory calprotectin protein in wild-type and transgenic enriched environment conditions compared to standard environment.\n\nConclusionOur data suggest that enriched social environment has a positive effect on the induction of SNCA mediated inflammation in the intestine by changing anti-inflammatory gut bacteria.

neuroscience↗

bioRxiv2017

MEGAN-LR: New algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs

BackgroundThere are numerous computational tools for taxonomic or functional analysis of microbiome samples, optimized to run on hundreds of millions of short, high quality sequencing reads. Programs such as MEGAN allow the user to interactively navigate these large datasets. Long read sequencing technologies continue to improve and produce increasing numbers of longer reads (of varying lengths in the range of 10k-1M bps, say), but of low quality. There is an increasing interest in using long reads in microbiome sequencing and there is a need to adapt short read tools to long read datasets.\n\nMethodsWe describe a new LCA-based algorithm for taxonomic binning, and an interval-tree based algorithm for functional binning, that are explicitly designed for long reads and assembled contigs. We provide a new interactive tool for investigating the alignment of long reads against reference sequences. For taxonomic and functional binning, we propose to use LAST to compare long reads against the NCBI-nr protein reference database so as to obtain frame-shift aware alignments, and then to process the results using our new methods.\n\nResultsAll presented methods are implemented in the open source edition of MEGAN and we refer to this new extension as MEGAN-LR (MEGAN long read). We evaluate the LAST+MEGAN-LR approach in a simulation study, and on a number of mock community datasets consisting of Nanopore reads, PacBio reads and assembled PacBio reads. We also illustrate the practical application on a Nanopore dataset that we sequenced from an anammox bio-rector community.

bioinformatics↗