Mitochondrial genome of the fluke pond snail, Austropeplea cf. brazieri (Gastropoda: Lymnaeidae).

BACKGROUND: Lymnaeid snails of the genus Austropeplea are an important vector of the liver fluke (Fasciola hepatica), contributing to livestock production losses in Australia and New Zealand. However, the species status within Austropeplea is ambiguous due to heavy reliance on morphological analysis and a relative lack of genetic data. This study aimed to characterise the mitochondrial genome of A. cf. brazieri, an intermediate host of liver fluke in eastern Victoria. METHODS: The mitochondrial genome was assembled and annotated from a combination of second- and third-generation sequencing data. For comparative purposes, we performed phylogenetic analyses of the concatenated nucleotide sequences of the mitochondrial protein-coding genes, cytochrome c oxidase subunit 1 and 16S genes. RESULTS: The assembled mt genome was 13,757 base pairs and comprised 37 genes, including 13 protein-coding genes, 22 transfer RNA genes and 2 ribosomal RNA genes. The mt genome length, gene order and nucleotide compositions were similar to related species of lymnaeids. Phylogenetic analyses of the mt nucleotide sequences placed A. cf. brazieri within the same clade as Orientogalba ollula with strong statistical supports. Phylogenies of the cox1 and 16S mt sequences were constructed due to the wide availability of these sequences representing the lymnaeid taxa. As expected in both these phylogenies, A. cf. brazieri clustered with other Austropeplea sequences, but the nodal supports were low. CONCLUSIONS: The representative mt genome of A. cf. brazieri should provide a useful resource for future molecular, epidemiology and parasitological studies of this socio-economically important lymnaeid species.

Enhanced Genomic and Transcriptomic Resources for Trichinella pseudospiralis and T. spiralis to Underpin the Discovery of Molecular Differences between Stages and Species.

Nematodes of the genus Trichinella are important pathogens of humans and animals. This study aimed to enhance the genomic and transcriptomic resources for T. pseudospiralis (non-encapsulated phenotype) and T. spiralis (encapsulated phenotype) and to explore transcriptional profiles. First, we improved the assemblies of the genomes of T. pseudospiralis (code ISS13) and T. spiralis (code ISS534), achieving genome sizes of 56.6 Mb (320 scaffolds, and an N50 of 1.02 Mb) and 63.5 Mb (568 scaffolds, and an N50 value of 0.44 Mb), respectively. Then, for each species, we produced RNA sequence data for three key developmental stages (first-stage muscle larvae [L1s], adults, and newborn larvae [NBLs]; three replicates for each stage), analysed differential transcription between stages, and explored enriched pathways and processes between species. Stage-specific upregulation was linked to cellular processes, metabolism, and host-parasite interactions, and pathway enrichment analysis showed distinctive biological processes and cellular localisations between species. Indeed, the secreted molecules calmodulin, calreticulin, and calsyntenin-with possible roles in modulating host immune responses and facilitating parasite survival-were unique to T. pseudospiralis and not detected in T. spiralis. These insights into the molecular mechanisms of Trichinella-host interactions might offer possible avenues for developing new interventions against trichinellosis.

Inference of Essential Genes of the Parasite Haemonchus contortus via Machine Learning.

Over the years, comprehensive explorations of the model organisms Caenorhabditis elegans (elegant worm) and Drosophila melanogaster (vinegar fly) have contributed substantially to our understanding of complex biological processes and pathways in multicellular organisms generally. Extensive functional genomic-phenomic, genomic, transcriptomic, and proteomic data sets have enabled the discovery and characterisation of genes that are crucial for life, called 'essential genes'. Recently, we investigated the feasibility of inferring essential genes from such data sets using advanced bioinformatics and showed that a machine learning (ML)-based workflow could be used to extract or engineer features from DNA, RNA, protein, and/or cellular data/information to underpin the reliable prediction of essential genes both within and between C. elegans and D. melanogaster. As these are two distantly related species within the Ecdysozoa, we proposed that this ML approach would be particularly well suited for species that are within the same phylum or evolutionary clade. In the present study, we cross-predicted essential genes within the phylum Nematoda (evolutionary clade V)-between C. elegans and the pathogenic parasitic nematode H. contortus-and then ranked and prioritised H. contortus proteins encoded by these genes as intervention (e.g., drug) target candidates. Using strong, validated predictors, we inferred essential genes of H. contortus that are involved predominantly in crucial biological processes/pathways including ribosome biogenesis, translation, RNA binding/processing, and signalling and which are highly transcribed in the germline, somatic gonad precursors, sex myoblasts, vulva cell precursors, various nerve cells, glia, or hypodermis. The findings indicate that this in silico workflow provides a promising avenue to identify and prioritise panels/groups of drug target candidates in parasitic nematodes for experimental validation in vitro and/or in vivo.

Characterisation of the tracheal transcriptional response of chickens to chronic infection with Mycoplasma synoviae.

Mycoplasma synoviae causes infectious synovitis and respiratory tract infections in chickens and is responsible for significant economic losses in the poultry industry. Effective attachment and colonisation of the trachea is critical for the persistence of the organism and progression of the disease it causes. The respiratory tract infection is usually sub-clinical, but concurrent infection with infectious bronchitis virus (IBV) is known to enhance the pathogenicity of M. synoviae. This study aimed to explore differentially expressed genes in the tracheal mucosa, and their functional categories, during chronic infection with M. synoviae, using a M. synoviae-IBV infection model. The transcriptional profiles of the trachea were assessed 2 weeks after infection using RNA sequencing. In chickens infected with M. synoviae or IBV, only 1 or 8 genes were differentially expressed compared to uninfected chickens, respectively. In contrast, the M. synoviae-IBV infected chickens had 621 upregulated and 206 downregulated genes compared to uninfected chickens. Upregulated genes and their functional categories were suggestive of uncontrolled lymphoid cell proliferation and an ongoing pro-inflammatory response. Genes associated with anti-inflammatory effects, pathogen removal, apoptosis, regulation of the immune response, airway homoeostasis, cell adhesion and tissue regeneration were downregulated. Overall, transcriptional changes in the trachea, 2 weeks after infection with M. synoviae and IBV, indicate immune dysregulation, robust inflammation and a lack of cytotoxic damage during chronic infection. This model provides insights into the pathogenesis of chronic infection with M. synoviae.

Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates.

Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus - a highly pathogenic, C. elegans-related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans, we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus. Two of these genes (i.e. Hc-eef-1A and Hc-eef1G), coding for eukaryotic elongation factors (called Hc-eEF1A and Hc-eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc-eEF1A and/or Hc-eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus. Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus, and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates.

Transcriptomic analysis of the effects of tylosin on the protective immunity provided by the Mycoplasma gallisepticum vaccine Vaxsafe MG ts-304.

The antimicrobial tylosin is commonly used to control mycoplasma infections, sometimes in combination with vaccination. However, the efficacy of a live mycoplasma vaccine, when combined with subsequent antimicrobial treatment, against the effects of subsequent infection with a virulent strain is unknown. This study employed differential gene expression analysis to evaluate the effects of tylosin on the protection provided by the live attenuated Vaxsafe MG ts-304 vaccine, which has been shown to be safe and to provide long-term protective immunity against infection with Mycoplasma gallisepticum. The transcriptional profiles of the tracheal mucosa revealed significantly enhanced inflammation, immune cell proliferation and adaptive immune responses in unvaccinated, untreated birds and in unvaccinated birds treated with tylosin 2 weeks after infection with virulent M. gallisepticum. These responses, indicative of the typical immune dysregulation caused by infection with M. gallisepticum, were less severe in the unvaccinated, tylosin-treated birds than in the unvaccinated, untreated birds. This was attributable to the effect of residual levels of tylosin in the tracheal mucosa on replication of virulent M. gallisepticum. These responses were not detected in vaccinated, tylosin-treated birds or in vaccinated, untreated birds after infection. The tracheal mucosal transcriptional profiles of these birds resembled those of unvaccinated, untreated, uninfected birds, suggesting a rapid and protective secondary immune response and effective vaccination. Overall, these results show that, although tylosin treatment reduced the duration of immunity, the initial protective immunity induced by Vaxsafe MG ts-304 lasted for at least 22 weeks after vaccination, even after the administration of tylosin for 16 weeks following vaccination.

Structure-activity relationship and target investigation of 2-aryl quinolines with nematocidal activity.

Within the context of our anthelmintic discovery program, we recently identified and evaluated a quinoline derivative, called ABX464 or obefazimod, as a nematocidal candidate; synthesised a series of analogues which were assessed for activity against the free-living nematode Caenorhabditis elegans; and predicted compound-target relationships by thermal proteome profiling (TPP) and in silico docking. Here, we logically extended this work and critically evaluated the anthelmintic activity of ABX464 analogues on Haemonchus contortus (barber's pole worm) - a highly pathogenic nematode of ruminant livestock. First, we tested a series of 44 analogues on H. contortus (larvae and adults) to investigate the nematocidal pharmacophore of ABX464, and identified one compound with greater potency than the parent compound and showed moderate activity against a select number of other parasitic nematodes (including Ancylostoma, Heligmosomoides and Strongyloides species). Using TPP and in silico modelling studies, we predicted protein HCON_00074590 (a predicted aldo-keto reductase) as a target candidate for ABX464 in H. contortus. Future work aims to optimise this compound as a nematocidal candidate and investigate its pharmacokinetic properties. Overall, this study presents a first step toward the development of a new nematocide.

Development and validation of a long-read metabarcoding platform for the detection of filarial worm pathogens of animals and humans.

BACKGROUND: Filarial worms are important vector-borne pathogens of a large range of animal hosts, including humans, and are responsible for numerous debilitating neglected tropical diseases such as, lymphatic filariasis caused by Wuchereria bancrofti and Brugia spp., as well as loiasis caused by Loa loa. Moreover, some emerging or difficult-to-eliminate filarioid pathogens are zoonotic using animals like canines as reservoir hosts, for example Dirofilaria sp. 'hongkongensis'. Diagnosis of filariasis through commonly available methods, like microscopy, can be challenging as microfilaremia may wane below the limit of detection. In contrast, conventional PCR methods are more sensitive and specific but may show limited ability to detect coinfections as well as emerging and/or novel pathogens. Use of deep-sequencing technologies obviate these challenges, providing sensitive detection of entire parasite communities, whilst also being better suited for the characterisation of rare or novel pathogens. Therefore, we developed a novel long-read metabarcoding assay for deep-sequencing the filarial nematode cytochrome c oxidase subunit I gene on Oxford Nanopore Technologies' (ONT) MinION™ sequencer. We assessed the overall performance of our assay using kappa statistics to compare it to commonly used diagnostic methods for filarial worm detection, such as conventional PCR (cPCR) with Sanger sequencing and the microscopy-based modified Knott's test (MKT). RESULTS: We confirmed our metabarcoding assay can characterise filarial parasites from a diverse range of genera, including, Breinlia, Brugia, Cercopithifilaria, Dipetalonema, Dirofilaria, Onchocerca, Setaria, Stephanofilaria and Wuchereria. We demonstrated proof-of-concept for this assay by using blood samples from Sri Lankan dogs, whereby we identified infections with the filarioids Acanthocheilonema reconditum, Brugia sp. Sri Lanka genotype and zoonotic Dirofilaria sp. 'hongkongensis'. When compared to traditionally used diagnostics, such as the MKT and cPCR with Sanger sequencing, we identified an additional filarioid species and over 15% more mono- and coinfections. CONCLUSIONS: Our developed metabarcoding assay may show broad applicability for the metabarcoding and diagnosis of the full spectrum of filarioids from a wide range of animal hosts, including mammals and vectors, whilst the utilisation of ONT' small and portable MinION™ means that such methods could be deployed for field use.

Structure activity relationship and target prediction for ABX464 analogues in Caenorhabditis elegans.

Global challenges with treatment failures and/or widespread resistance in parasitic worms against commercially available anthelmintics lend impetus to the development of new anthelmintics with novel mechanism(s) of action. The free-living nematode Caenorhabditis elegans is an important model organism used for drug discovery, including the screening and structure-activity investigation of new compounds, and target deconvolution. Previously, we conducted a whole-organism phenotypic screen of the 'Pandemic Response Box' (from Medicines for Malaria Venture, MMV) and identified a hit compound, called ABX464, with activity against C. elegans and a related, parasitic nematode, Haemonchus contortus. Here, we tested a series of 44 synthesized analogues to explore the pharmacophore of activity on C. elegans and revealed five compounds whose potency was similar or greater than that of ABX464, but which were not toxic to human hepatoma (HepG2) cells. Subsequently, we employed thermal proteome profiling (TPP), protein structure prediction and an in silico-docking algorithm to predict ABX464-target candidates. Taken together, the findings from this study contribute significantly to the early-stage drug discovery of a new nematocide based on ABX464. Future work is aimed at validating the ABX464-protein interactions identified here, and at assessing ABX464 and associated analogues against a panel of parasitic nematodes, towards developing a new anthelmintic with a mechanism of action that is distinct from any of the compounds currently-available commercially.

'Bingo'-a large language model- and graph neural network-based workflow for the prediction of essential genes from protein data.

The identification and characterization of essential genes are central to our understanding of the core biological functions in eukaryotic organisms, and has important implications for the treatment of diseases caused by, for example, cancers and pathogens. Given the major constraints in testing the functions of genes of many organisms in the laboratory, due to the absence of in vitro cultures and/or gene perturbation assays for most metazoan species, there has been a need to develop in silico tools for the accurate prediction or inference of essential genes to underpin systems biological investigations. Major advances in machine learning approaches provide unprecedented opportunities to overcome these limitations and accelerate the discovery of essential genes on a genome-wide scale. Here, we developed and evaluated a large language model- and graph neural network (LLM-GNN)-based approach, called 'Bingo', to predict essential protein-coding genes in the metazoan model organisms Caenorhabditis elegans and Drosophila melanogaster as well as in Mus musculus and Homo sapiens (a HepG2 cell line) by integrating LLM and GNNs with adversarial training. Bingo predicts essential genes under two 'zero-shot' scenarios with transfer learning, showing promise to compensate for a lack of high-quality genomic and proteomic data for non-model organisms. In addition, the attention mechanisms and GNNExplainer were employed to manifest the functional sites and structural domain with most contribution to essentiality. In conclusion, Bingo provides the prospect of being able to accurately infer the essential genes of little- or under-studied organisms of interest, and provides a biological explanation for gene essentiality.