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The intestinal nematodePseudocapillaria tomentosain zebrafish (Danio rerio) causes profound intestinal lesions, emaciation and death and is a promoter of a common intestinal cancer in zebrafish. This nematode has been detected in zebrafish from about 15% of the laboratories. Adult worms are readily detected about 3 weeks after exposure by either histology or wet mount preparations of the intestine, and larval worms are inconsistently observed in fish before this time. A quantitative PCR (qPCR) test was recently developed to detect the worm in fish and water, and here we determined that the test on zebrafish intestines was effective for earlier detection. Four lines of zebrafish (AB, TU, 5D and Casper) were experimentally infected and evaluated by wet mounts and qPCR at 8, 15‐, 22‐, 31‐ and 44‐day post‐exposure (dpe). At the first two time points, only 8% of the wet mounts from exposed fish were identified as infected, while the same intestines screened by qPCR showed 78% positivity, with low and consistent cycle threshold (Ct) values at these times. Wet mounts at later time points showed a high prevalence of infection, but this was still surpassed by qPCR.

 

ABSTRACT A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts. 

ABSTRACT Here, we report the draft genome sequence of Pseudomonas sp. strain DrBHI1. The total assembly length is 5,649,751 bp in 146 contigs. This strain was isolated from zebrafish ( Danio rerio ) feces.