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18 results for “fish pathogens”
Dataset for Multiplex-PCR detection and Nanopore-based genotyping of fish pathogens
<p>This is a revised zip file contains scripts, initial fastq files, assembled amplicon (public and from this study) as well as bioinformatics intermediate files used for this study.</p> <p>Changelog:</p> <p>1. Fixed a bug in the 02_consensus.sh to enable proper removal of amplicons with zero depth</p> <p>2. Added a script (06_unclassified_read.sh) to extract and annotate reads that previously could not align to the 4 reference gene segment. Now the previously unclassified reads will be re-align (raw fastq) back to the gene segments as well as an additional tilapia genome assembly to gauge amount of reads mapping to the host genome. Furthermore, any read that still fail to align with minimap2 was subsequently aligned using blastn (-word_size 15 -evalue 0.01) against the same sequences.</p> <p>File Structure and Descriptions</p> <p>├── 01_process.sh : primer trimming, length-based filtering, read alignment, alignment filtering (unique hit) and extraction of uniquely hit reads for consensus generation<br> ├── 02_consensus.sh : [need artic conda env] Generation of consensus based on uniquely-mapped reads and minimal read depth of 20x required to call a variant (or it will be masked)<br> ├── 03_cleanup.sh: General folder and intermediate file re-organization<br> ├── 04_filter.sh: [need quast conda env] statistic of consensus generated and filtering of consensus with one or more ambiguous base (N), not suitable for haplotype<br> ├── 05_cluster.sh: clustering of consensus based on 100% identity threshold to generate putative haplotype<br> ├── 06_unclassified_read.sh: Extraction and annotation of unclassified reads using lenient criteria and with host reference genome as added reference<br> ├── Amplicon_FastQ folder: uniquely mapped fastq files for consensus generation<br> ├── BAM: alignment files generated from minimap2 used as input for the artic pipeline to identify variants<br> ├── Cluster_Rep.txt: Consensus sequences that were chosen to represent each haplotype<br> ├── Consensus folder: consensus fasta files generated for each sample containing sequences for each specific pathogen<br> ├── Coverage folder: coverage and base-level read depth for each sample and each pathogen reference genes<br> ├── Filter: individual fasta sequences (only 1 sequence per file) for each pathogen and each sample without any ambiguous base for subsequent clustering analysis<br> ├── Full_Haplotype.fasta: all possible haplotype sequences generated for each pathogen<br> ├── Gap_Analysis.tsv: Table with percentage of gap (0-100%) for each consensus sequence generated (used for filtering)<br> ├── Haplotype folder: Intermediate file and sample-level haplotype used to infer final haplotype and generate haplotype summary<br> ├── Haplotype_summary.tsv: Table with sample ID and their respectively pathogen haplotype<br> ├── Minimap2_PAF: Intermediate alignment generated from minimap2 used to generate the count table<br> ├── FailMinimap2 folder: FastQ files that didn't align using minimap2. Will be subsequently aligned using blastN (more sensitive) against the same reference sequences as minimap2<br> ├── Host_4Pathogen.fasta: Fasta file containing the tilapia genome and 4 pathogen (primer binding site included)<br> ├── Original: fastq with original naming prior to renaming based on sampleID. a script (rename.sh) was included to show renaming scheme<br> ├── primer.fasta: Primer sequences used for identifying and trimming reads with flanking primer sequence<br> ├── primer.fasta.fai: the index file for primer.fasta<br> ├── PrimerTrim folder: Primer-trimmed reads<br> ├── quast_results: consensus statistics generated by quast<br> ├── RawCount.tsv: Count table generated that can used as a input to generate figure<br> ├── RawFastq folder: Raw reads that have been renamed to reflect sample information<br> ├── readme.md: The current readme file<br> ├── ref_full_latest.fasta: Reference sequence of (gene segments) 4 pathogens e.g. TilV, ISKNV, SAG (Streptococcus agalactiae), FNO (Francisella noatunensis subsp. orientalis)<br> ├── ref_full_latest.primer.fasta: Same as above but with their primer binding sequence trimmed similar to the processed reads<br> ├── ref_full_latest.primer.fasta.fai<br> ├── RenameHaplotype: Script to perform reorganization of cdhit output<br> ├── Seq.stat.tsv: Sequencing statistics<br> ├── Uniq_PAF: Minimap2 alignment file for raw reads that initially failed quality check (no primer present and/or less than 80% query coverage / not unique alignment)<br> ├── Unmap: Raw reads that initial failed quality check (no primer on both ends / less than 80% query coverage / not unique alignment) <br> └── VCF: VCF files from medaka variant calling used to generate the final consensus</p>
Seagrass ecosystems reduce exposure to bacterial pathogens of humans, fishes and invertebrates
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Eco-evolutionary responses of a cold-water fish (Salmo trutta) to the combined effects of an emerging pathogen and temperature
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Data from: Higher resource level promotes virulence in an environmentally transmitted bacterial fish pathogen
Diseases have become a primary constraint to sustainable aquaculture, but remarkably little attention has been paid to a broad class of pathogens: the opportunists. Opportunists often persist in the environment outside the host and their pathogenic features are influenced by changes in the environment. To test how environmental nutrient levels influence virulence, we used strains of Flavobacterium columnare, an environmentally transmitted fish pathogen, to infect rainbow trout and zebra fish in two different nutrient concentrations. To separate the effects of dose and nutrients, we used three infective doses and studied the growth of bacteria in vitro. High nutrient concentration promoted both the virulence and the outside-host growth of the pathogen, most notably in a low-virulence strain. The increase in virulence could not be exhaustively explained by the increased dose under higher nutrient supply, suggesting virulence factor activation. In aquaculture settings, accumulation of organic material in rearing units can locally increase water nutrient concentration and therefore increase disease risk as a response to elevated bacterial density and virulence factor activation. Our results highlight the role of increased nutrients in outside-host environment as a selective agent for higher virulence and faster evolutionary rate in opportunistic pathogens.
Data from: Rich resource environment of fish farms facilitates phenotypic variation and virulence in an opportunistic fish pathogen
<p><span>Phenotypic variation is suggested to facilitate the persistence of environmentally growing pathogens under environmental change. Here we hypothesized that the intensive farming environment induces higher phenotypic variation in microbial pathogens than natural environment, because of high stochasticity for growth and stronger survival selection compared to the natural environment. We tested the hypothesis with an opportunistic fish pathogen <em>Flavobacterium columnare</em> isolated either from fish farms or from natural waters. We measured growth parameters of two morphotypes from all isolates in different resource concentrations and two temperatures relevant for the occurrence of disease epidemics at farms and tested their virulence using a zebrafish (<em>Danio rerio</em>) infection model. According to our hypothesis, isolates originating from the fish farms had higher phenotypic variation in growth between the morphotypes than the isolates from natural waters. The difference was more pronounced in higher resource concentrations and the higher temperature, suggesting that phenotypic variation is driven by the exploitation of increased outside-host resources at farms. Phenotypic variation of virulence was not observed based on isolate origin but only based on morphotype. However, when in contact with the larger fish, the less virulent morphotype of some of the isolates also had high virulence. As the less virulent morphotype also had higher growth rate in outside-host resources, the results suggest that both morphotypes can contribute to <em>F. columnare</em> epidemics at fish farms, especially with current prospects of warming temperatures. Our results suggest that higher phenotypic variation per se does not lead to higher virulence, but that environmental conditions at fish farms could select isolates with high phenotypic variation in bacterial population and hence affect evolution in <em>F. columnare</em> at fish farms. Our results highlight the multifaceted effects of human-induced environmental alterations in </span><span>shaping epidemiology and evolution in microbial</span><span> pat</span><span>hogens.</span></p> <p> </p>
FIGURE 3 in Monogenea on exotic Indian freshwater fish. 5. First report of pathogenic Gussevia asota (Platyhelminths) from Oscar Astronotus ocellatus (Agassiz 1831) (Perciformes: Cichlidae)
FIGURE 3. Phylogenetic tree inferred from ML method based on partial 28S rDNA gene sequences of Gussevia asota Kritsky et al., 1989. Numbers preceding the taxa are GenBank accession numbers for their 28s rRNA gene sequences. Bootstrap values (1000 replicates) are shown above nodes. Quadriacanthus bagrae and Thaparocleidus siluri were used as the outgroups. The taxon denoted by a black dot corresponds to the sequence generated in this study.
FIGURE 1 in Monogenea on exotic Indian freshwater fish. 5. First report of pathogenic Gussevia asota (Platyhelminths) from Oscar Astronotus ocellatus (Agassiz 1831) (Perciformes: Cichlidae)
FIGURE 1. Line drawings of sclerotised parts of Gussevia asota Kritsky et al., 1989 from Astronauts ocellatus (Agassiz 1831). A. Dorsal anchor. B. Ventral anchor. C. Hook pair 5. D. Hook pairs 1–4, 6, 7. E. Dorsal bar. F. Ventral bar. G. Vagina. H and I. Male copulatory organ (different configurations) with a prostatic reservoir and a seminal vesicle. Scale bar = 30 μm.
FIGURE 2 in Monogenea on exotic Indian freshwater fish. 5. First report of pathogenic Gussevia asota (Platyhelminths) from Oscar Astronotus ocellatus (Agassiz 1831) (Perciformes: Cichlidae)
FIGURE 2. Phase contrast, DIC, and light microscopy images of sclerotised parts of Gussevia asota Kritsky et al., 1989 from Astronotus ocellatus (Agassiz 1831). A. Haptoral anchor/bar complex and hooks. B. Male copulatory organ. C and D. Vagina. E. Hook pairs 1–4, 6, 7. F. Haptoral lobes. Scale bar: A, B, C, D, F=20 μm; E=10 μm.
Data from: Higher resource level promotes virulence in an environmentally transmitted bacterial fish pathogen
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Data from: Rich resource environment of fish farms facilitates phenotypic variation and virulence in an opportunistic fish pathogen
Open the record for dataset details and reuse information.
Transcriptome and proteome of fish-pathogenic Streptococcus agalactiae are modulated by temperature change
GEO Series GSE112416. Streptococcus agalactiae. 6 samples. Type: Expression profiling by array.
Transcriptional responses of resistant and susceptible fish clones to the bacterial pathogen Flavobacterium psychrophilum
GEO Series GSE35448. Oncorhynchus mykiss. 23 samples. Type: Expression profiling by array.
A transcriptomic study reveals that fish vibriosis due to the zoonotic pathogen Vibrio vulnificus is an acute inflammatory disease.
GEO Series GSE196944. Anguilla anguilla. 63 samples. Type: Expression profiling by array.
Expression profiling of a spf deletion mutant suggests biological roles and mRNA targets for Spot 42 in the fish pathogen Aliivibrio salmonicida
GEO Series GSE28087. Aliivibrio salmonicida LFI1238; Aliivibrio salmonicida. 3 samples. Type: Expression profiling by array.
Transcriptome architecture and regulation at environmental transitions of the fish pathogen Flavobacterium psychrophilum: Global RNA-Seq
GEO Series GSE164190. Flavobacterium psychrophilum. 1 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome architecture and regulation at environmental transitions of the fish pathogen Flavobacterium psychrophilum: 5’-end RNA-Seq
GEO Series GSE164189. Flavobacterium psychrophilum. 1 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome architecture and regulation at environmental transitions of the fish pathogen Flavobacterium psychrophilum
GEO Series GSE163842. Flavobacterium psychrophilum. 64 samples. Type: Expression profiling by array.
The human pathogen Mycobacterium tuberculosis and the fish pathogen Mycobacterium marinum trigger the same core set of late immune response genes in zebrafish larvae
GEO Series GSE252417. Danio rerio. 32 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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OpenNeuro
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