Project description:In livestock species, the monolayer of epithelial cells covering the digestive mucosa plays an essential role for nutrition and gut barrier function. However, research on farm animal intestinal epithelium has been hampered by the lack of appropriate in vitro models. Over the past decade, methods to culture livestock intestinal organoids have been developed in pig, bovine, rabbit, horse, sheep and chicken. Gut organoids from farm animals are obtained by seeding tissue-derived intestinal epithelial stem cells in a 3-dimensional culture environment reproducing in vitro the stem cell niche. These organoids can be generated rapidly within days and are formed by a monolayer of polarized epithelial cells containing the diverse differentiated epithelial progeny, recapitulating the original structure and function of the native epithelium. The phenotype of intestinal organoids is stable in long-term culture and reflects characteristics of the digestive segment of origin. Farm animal intestinal organoids can be amplified in vitro, cryopreserved and used for multiple experiments, allowing an efficient reduction of the use of live animals for experimentation. Most of the studies using livestock intestinal organoids were used to investigate host-microbe interactions at the epithelial surface, mainly focused on enteric infections with viruses, bacteria or parasites. Numerous other applications of farm animal intestinal organoids include studies on nutrient absorption, genome editing and bioactive compounds screening relevant for agricultural, veterinary and biomedical sciences. Further improvements of the methods used to culture intestinal organoids from farm animals are required to replicate more closely the intestinal tissue complexity, including the presence of non-epithelial cell types and of the gut microbiota. Harmonization of the methods used to culture livestock intestinal organoids will also be required to increase the reproducibility of the results obtained in these models. In this review, we summarize the methods used to generate and cryopreserve intestinal organoids in farm animals, present their phenotypes and discuss current and future applications of this innovative culture system of the digestive epithelium.
Project description:Viruses closely related to Norwalk-like viruses (NLVs) were recently found in stored stool samples from two calves (United Kingdom and Germany) and four pigs (Japan), sparking discussions about the potential for zoonotic transmission. To investigate if NLVs are commonly present in farm animals, pooled stool samples from 100 pig farms, 48 chicken farms, 43 dairy cow herds, and 75 veal calf farms from the Netherlands were assayed by reverse transcription- polymerase chain reaction amplification, using primers specific for the detection of NLVs from humans. NLV RNA was detected in 33 (44%) of the specimens from veal calf farms and two (2%) specimens from pig farms. Our data show that NLV infections until recently thought to be restricted to humans occur often in calves and sometimes in pigs. While zoonotic transmission has not been proven, these findings suggest that calves and pigs may be reservoir hosts of NLVs.
Project description:IntroductionThe field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools.ObjectivesTo compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics.MethodsThe most widely used tools were selected for inclusion in the review by either ? 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC-MS, GC-MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for.ResultsA comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary.ConclusionThis review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools' abilities to perform specific data analysis tasks e.g. peak picking.
Project description:To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.
Project description:The evolution of sequencing technology has lead to an enormous increase in the number of genomes that have been sequenced. This is especially true in the field of virus genomics. In order to extract meaningful biological information from these genomes, whole genome data mining software tools must be utilized. Hundreds of tools have been developed to analyze biological sequence data. However, only some of these tools are user-friendly to biologists. Several of these tools that have been successfully used to analyze adenovirus genomes are described here. These include Artemis, EMBOSS, pDRAW, zPicture, CoreGenes, GeneOrder, and PipMaker. These tools provide functionalities such as visualization, restriction enzyme analysis, alignment, and proteome comparisons that are extremely useful in the bioinformatics analysis of adenovirus genomes.
Project description:Microinjection of DNA is now the most widespread method for generating transgenic animals, but transgenesis rates achieved this way in higher mammals are extremely low. To address this longstanding problem, we used lentiviral vectors carrying a ubiquitously active promoter (phosphoglycerate kinase, LV-PGK) to deliver transgenes to porcine embryos. Of the 46 piglets born, 32 (70%) carried the transgene DNA and 30 (94%) of these pigs expressed the transgene (green fluorescent protein, GFP). Direct fluorescence imaging and immunohistochemistry showed that GFP was expressed in all tissues of LV-PGK transgenic pigs, including germ cells. Importantly, the transgene was transmitted through the germ-line. Tissue-specific transgene expression was achieved by infecting porcine embryos with lentiviral vectors containing the human keratin K14 promoter (LV-K14). LV-K14 transgenic animals expressed GFP specifically in basal keratinocytes of the skin. Finally, infection of bovine oocytes after and before in vitro fertilization with LV-PGK resulted in transgene expression in 45% and 92% of the infected embryos, respectively.
Project description:A majority of emerging infectious diseases are of zoonotic origin. Metagenomic Next-Generation Sequencing (mNGS) has been employed to identify uncommon and novel infectious etiologies and characterize virus diversity in human, animal, and environmental samples. Here, we systematically reviewed studies that performed viral mNGS in common livestock (cattle, small ruminants, poultry, and pigs). We identified 2481 records and 120 records were ultimately included after a first and second screening. Pigs were the most frequently studied livestock and the virus diversity found in samples from poultry was the highest. Known animal viruses, zoonotic viruses, and novel viruses were reported in available literature, demonstrating the capacity of mNGS to identify both known and novel viruses. However, the coverage of metagenomic studies was patchy, with few data on the virome of small ruminants and respiratory virome of studied livestock. Essential metadata such as age of livestock and farm types were rarely mentioned in available literature, and only 10.8% of the datasets were publicly available. Developing a deeper understanding of livestock virome is crucial for detection of potential zoonotic and animal pathogens and One Health preparedness. Metagenomic studies can provide this background but only when combined with essential metadata and following the "FAIR" (Findable, Accessible, Interoperable, and Reusable) data principles.
Project description:Imaging and analyzing the locomotion behavior of small animals such as Drosophila larvae or C. elegans worms has become an integral subject of biological research. In the past we have introduced FIM, a novel imaging system feasible to extract high contrast images. This system in combination with the associated tracking software FIMTrack is already used by many groups all over the world. However, so far there has not been an in-depth discussion of the technical aspects. Here we elaborate on the implementation details of FIMTrack and give an in-depth explanation of the used algorithms. Among others, the software offers several tracking strategies to cover a wide range of different model organisms, locomotion types, and camera properties. Furthermore, the software facilitates stimuli-based analysis in combination with built-in manual tracking and correction functionalities. All features are integrated in an easy-to-use graphical user interface. To demonstrate the potential of FIMTrack we provide an evaluation of its accuracy using manually labeled data. The source code is available under the GNU GPLv3 at https://github.com/i-git/FIMTrack and pre-compiled binaries for Windows and Mac are available at http://fim.uni-muenster.de.