Hohenheim Center for Livestock Microbiome Research (HoLMiR)

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/17567

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Browsing Hohenheim Center for Livestock Microbiome Research (HoLMiR) by Subject "16S rRNA gene"

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    Methane reduction potential of brown seaweeds and their influence on nutrient degradation and microbiota composition in a rumen simulation technique
    (2022) Künzel, Susanne; Yergaliyev, Timur; Wild, Katharina J.; Philippi, Hanna; Petursdottir, Asta H.; Gunnlaugsdottir, Helga; Reynolds, Chris K.; Humphries, David J.; Camarinha-Silva, Amélia; Rodehutscord, Markus
    This study aimed to investigate the effects of two brown Icelandic seaweed samples (Ascophyllum nodosum and Fucus vesiculosus) on in vitro methane production, nutrient degradation, and microbiota composition. A total mixed ration (TMR) was incubated alone as control or together with each seaweed at two inclusion levels (2.5 and 5.0% on a dry matter basis) in a long-term rumen simulation technique (Rusitec) experiment. The incubation period lasted 14 days, with 7 days of adaptation and sampling. The methane concentration of total gas produced was decreased at the 5% inclusion level of A. nodosum and F. vesiculosus by 8.9 and 3.6%, respectively (P < 0.001). The total gas production was reduced by all seaweeds, with a greater reduction for the 5% seaweed inclusion level (P < 0.001). Feed nutrient degradation and the production of volatile fatty acids and ammonia in the effluent were also reduced, mostly with a bigger effect for the 5% inclusion level of both seaweeds, indicating a reduced overall fermentation (all P ≤ 0.001). Microbiota composition was analyzed by sequencing 16S rRNA amplicons from the rumen content of the donor cows, fermenter liquid and effluent at days 7 and 13, and feed residues at day 13. Relative abundances of the most abundant methanogens varied between the rumen fluid used for the start of incubation and the samples taken at day 7, as well as between days 7 and 13 in both fermenter liquid and effluent (P < 0.05). According to the differential abundance analysis with q2-ALDEx2, in effluent and fermenter liquid samples, archaeal and bacterial amplicon sequence variants were separated into two groups (P < 0.05). One was more abundant in samples taken from the treatment without seaweed supplementation, while the other one prevailed in seaweed supplemented treatments. This group also showed a dose-dependent response to seaweed inclusion, with a greater number of differentially abundant members between a 5% inclusion level and unsupplemented samples than between a 2.5% inclusion level and TMR. Although supplementation of both seaweeds at a 5% inclusion level decreased methane concentration in the total gas due to the high iodine content in the seaweeds tested, the application of practical feeding should be done with caution.
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    NaMeco - Nanopore full-length 16S rRNA gene reads clustering and annotation
    (2026) Yergaliyev, Timur; Rios-Galicia, Bibiana; Camarinha-Silva, Amélia; Yergaliyev, Timur; Institute of Animal Science, University of Hohenheim, Stuttgart, Germany; Rios-Galicia, Bibiana; Institute of Animal Science, University of Hohenheim, Stuttgart, Germany; Camarinha-Silva, Amélia; Institute of Animal Science, University of Hohenheim, Stuttgart, Germany
    Background: Nanopore sequencing is currently one of the leading third-generation sequencing technologies on the market and is gaining popularity among researchers. Due to its long-read capabilities, full-length 16S rRNA gene metabarcoding using Oxford Nanopore Technologies (ONT) offers great potential for metataxonomic studies. However, the relatively high error rate poses a significant challenge for bioinformatic processing, often limiting taxonomy resolution to the genus level despite the longer read length. Results: This study presents NaMeco, a novel tool specifically developed to efficiently process long 16S rRNA gene reads sequenced using Oxford Nanopore Technologies, requiring minimal user input. Our tool performs read quality control, primer-specific extraction of sequences and their clustering, followed by taxonomic annotation with percent identity thresholds that minimize the amount of false-positive annotations. It produces several outputs: a table of cluster counts, taxonomic annotations of clusters, their representative sequences in fasta format and taxa counts at each taxonomy rank. Output files are compatible with the Qiime2 pipeline and can be imported into the required format for downstream analyses. Conclusions: NaMeco, in combination with the full SSU GTDB database, outperforms existing tools such as NanoCLUST and EPI2ME, while delivering taxonomy accuracy and detection rates comparable to Emu.