Fakultätsübergreifend / Sonstige Einrichtung

Permanent URI for this communityhttps://hohpublica.uni-hohenheim.de/handle/123456789/36

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The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes
(2023) Avila Santos, Anderson Paulo; Kabiru Nata’ala, Muhammad; Kasmanas, Jonas Coelho; Bartholomäus, Alexander; Keller-Costa, Tina; Jurburg, Stephanie D.; Tal, Tamara; Camarinha-Silva, Amélia; Saraiva, João Pedro; Ponce de Leon Ferreira de Carvalho, André Carlos; Stadler, Peter F.; Sipoli Sanches, Danilo; Rocha, Ulisses
Background: Metagenomic data can shed light on animal-microbiome relationships and the functional potential of these communities. Over the past years, the generation of metagenomics data has increased exponentially, and so has the availability and reusability of data present in public repositories. However, identifying which datasets and associated metadata are available is not straightforward. We created the Animal-Associated Metagenome Metadata Database (AnimalAssociatedMetagenomeDB - AAMDB) to facilitate the identification and reuse of publicly available non-human, animal-associated metagenomic data, and metadata. Further, we used the AAMDB to (i) annotate common and scientific names of the species; (ii) determine the fraction of vertebrates and invertebrates; (iii) study their biogeography; and (iv) specify whether the animals were wild, pets, livestock or used for medical research. Results: We manually selected metagenomes associated with non-human animals from SRA and MG-RAST. Next, we standardized and curated 51 metadata attributes (e.g., host, compartment, geographic coordinates, and country). The AAMDB version 1.0 contains 10,885 metagenomes associated with 165 different species from 65 different countries. From the collected metagenomes, 51.1% were recovered from animals associated with medical research or grown for human consumption (i.e., mice, rats, cattle, pigs, and poultry). Further, we observed an over-representation of animals collected in temperate regions (89.2%) and a lower representation of samples from the polar zones, with only 11 samples in total. The most common genus among invertebrate animals was Trichocerca (rotifers). Conclusion: Our work may guide host species selection in novel animal-associated metagenome research, especially in biodiversity and conservation studies. The data available in our database will allow scientists to perform meta-analyses and test new hypotheses (e.g., host-specificity, strain heterogeneity, and biogeography of animal-associated metagenomes), leveraging existing data. The AAMDB WebApp is a user-friendly interface that is publicly available at https://webapp.ufz.de/aamdb/ .
DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics
(2021) Kienbaum, Lydia; Correa Abondano, Miguel; Blas, Raul; Schmid, Karl
Background: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy (r = 0.99). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10–20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. Conclusions: Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.
Microbiota and nutrient portraits of European roe deer (Capreolus capreolus) rumen contents in characteristic Southern German habitats
(2023) Dahl, Sarah-Alica; Seifert, Jana; Camarinha-Silva, Amélia; Cheng, Yu-Chieh; Hernández-Arriaga, Angélica; Hudler, Martina; Windisch, Wilhelm; König, Andreas
Roe deer ( Capreolus capreolus ) are found in various habitats, from pure forest cultures to agricultural areas and mountains. In adapting to the geographically and seasonally differentiating food supply, they depend, above all, on an adapted microbiome. However, knowledge about the microbiome of wild ruminants still needs to be improved. There are only a few publications for individual species with a low number of samples. This study aims to identify a core microbiota for Bavarian roe deer and present nutrient and microbiota portraits of the individual habitat types. This study investigated the roe deer’s rumen (reticulorumen) content from seven different characteristic Bavarian habitat types. The focus was on the composition of nutrients, fermentation products, and the rumen bacterial community. A total of 311 roe deer samples were analysed, with the most even possible distribution per habitat, season, age class, and gender. Significant differences in nutrient concentrations and microbial composition were identified for the factors habitat, season, and age class. The highest crude protein content (plant protein and microbial) in the rumen was determined in the purely agricultural habitat (AG), the highest value of non-fibre carbohydrates in the alpine mountain forest, and the highest fibre content (neutral detergent fibre, NDF) in the pine forest habitat. Maximum values for fibre content go up to 70% NDF. The proportion of metabolites (ammonia, lactate, total volatile fatty acids) was highest in the Agriculture-Beech-Forest habitat (ABF). Correlations can be identified between adaptations in the microbiota and specific nutrient concentrations, as well as in strong fluctuations in ingested forage. In addition, a core bacterial community comprising five genera could be identified across all habitats, up to 44% of total relative abundance. As with all wild ruminants, many microbial genera remain largely unclassified at various taxonomic levels. This study provides a more in-depth insight into the diversity and complexity of the roe deer rumen microbiota. It highlights the key microorganisms responsible for converting naturally available nutrients of different botanical origins.

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