Profiling the glycome of Cardicola forsteri, a blood fluke parasitic to bluefin tuna

Coff, Lachlan; Abrahams, Jodie L.; Collett, Simon; Power, Cecilia; Nowak, Barbara F.; Kolarich, Daniel; Bott, Nathan J.; Ramsland, Paul A.

doi:10.1016/j.ijpara.2021.06.004

Cited by 5 publications

(3 citation statements)

References 81 publications

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“…As expected, many of these are likely responsible for the synthesis of N-glycans, as indicated by structural domains in the CAZy database [52]. In its adult life cycle stage, C. forsteri predominately synthesizes oligomannose N-glycans, as well as paucimannosidic and complex-type N-glycans carrying core fucose and xylose [71]. No α1-3-linked fucoses were found in adults, but 3 putative α1-3 fucosyltransferases were identified in this study (Table 5).…”

Section: Plos Onesupporting

confidence: 59%

“…In particular, CfFucTD is homologous (sequence identity > 46%) to schistosomal α1-3 fucosyltransferases D (E2EAI7), E (E2EAI8), and F (E2EAI9), which have been functionally characterised and were shown to synthesize Lewis X and fucosylated LacdiNAc motifs [72,73]. Therefore, C. forsteri likely synthesizes these motifs in pre-adult life cycle stages, as postulated by Coff et al [71]. Sustained IgG1 and IgG3 responses to cercarial multifucosylated LDN motifs have been associated with protective immunity against schistosomiases [74][75][76][77], so glycomics is crucial to a holistic…”

Section: Plos Onementioning

confidence: 79%

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Draft genome of the bluefin tuna blood fluke, Cardicola forsteri

et al. 2022

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The blood fluke Cardicola forsteri (Trematoda: Aporocotylidae) is a pathogen of ranched bluefin tuna in Japan and Australia. Genomics of Cardicola spp. have thus far been limited to molecular phylogenetics of select gene sequences. In this study, sequencing of the C. forsteri genome was performed using Illumina short-read and Oxford Nanopore long-read technologies. The sequences were assembled de novo using a hybrid of short and long reads, which produced a high-quality contig-level assembly (N50 > 430 kb and L50 = 138). The assembly was also relatively complete and unfragmented, comprising 66% and 7.2% complete and fragmented metazoan Benchmarking Universal Single-Copy Orthologs (BUSCOs), respectively. A large portion (> 55%) of the genome was made up of intergenic repetitive elements, primarily long interspersed nuclear elements (LINEs), while protein-coding regions cover > 6%. Gene prediction identified 8,564 hypothetical polypeptides, > 77% of which are homologous to published sequences of other species. The identification of select putative proteins, including cathepsins, calpains, tetraspanins, and glycosyltransferases is discussed. This is the first genome assembly of any aporocotylid, a major step toward understanding of the biology of this family of fish blood flukes and their interactions within hosts.

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Section: Plos Onesupporting

confidence: 59%

Section: Plos Onementioning

confidence: 79%

Draft genome of the bluefin tuna blood fluke, Cardicola forsteri

et al. 2022

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“…To demonstrate that we could apply our developed methods to truly novel samples, we analyzed the serum N-glycome of southern bluefin tuna (Thunnus maccoyii), which was measured within GPST000182 36 but never reported in an annotated manner. This resulted in over 50 glycans, including high-mannose, hybrid, and complex structures, with features such as bisecting GlcNAc, core and antenna fucosylation, Neu5Gc, and multi-antennae N-glycans (Fig.…”

Section: Uncovering New Biological Insights Using Candycrunch and Can...mentioning

confidence: 99%

Predicting glycan structure from tandem mass spectrometry via deep learning

Urban

Jin

Thomsson

et al. 2023

Preprint

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Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 300,000 annotated MS/MS spectra, we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw LC-MS/MS data in seconds (Top1 Accuracy: 87.7%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification, and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb. We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.

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