New records of Hepatozoon and Oswaldofilaria from saltwater crocodiles (Crocodylus porosus) in Australia.

Diseases affecting wild Australian saltwater crocodiles (Crocodylus porosus) are rarely reported due to the difficulty in capturing animals and obtaining samples. In this investigation, we identified two haemoparasites (Hepatozoon and a filarial nematode) in saltwater crocodiles in Darwin, Australia. Light microscopic examination identified Hepatozoon in 7/7 (100%) wild crocodiles and in 2/20 (10%) of captive ones. When genomic DNAs from these same samples were further investigated using polymerase chain reaction (PCR)-based sequencing, we detected Hepatozoon in all 27 blood samples. Using both microscopy and PCR-based sequencing, we detected a filarial worm (proposed to be Oswaldofilaria) in one of 20 captive crocodiles. The sequence data were compared with sequence data available in public databases, and phylogenetic analyses indicated that the operational taxonomic units of Hepatozoon and Oswaldofilaria discovered here in these crocodiles are likely new species. This study is the first to use molecular tools to explore haemoparasites in Australian saltwater crocodiles and highlights the importance of health investigations in poorly studied vertebrate hosts.

Complete Mitochondrial Genome for Lucilia cuprina dorsalis (Diptera: Calliphoridae) from the Northern Territory, Australia.

The Australian sheep blowfly, Lucilia cuprina dorsalis, is a major sheep ectoparasite causing subcutaneous myiasis (flystrike), which can lead to reduced livestock productivity and, in severe instances, death of the affected animals. It is also a primary colonizer of carrion, an efficient pollinator, and used in maggot debridement therapy and forensic investigations. In this study, we report the complete mitochondrial (mt) genome of L. c. dorsalis from the Northern Territory (NT), Australia, where sheep are prohibited animals, unlike the rest of Australia. The mt genome is 15,943 bp in length, comprising 13 protein-coding genes (PCGs), two ribosomal RNAs (rRNAs), 22 transfer RNAs (tRNAs), and a non-coding control region. The gene order of the current mt genome is consistent with the previously published L. cuprina mt genomes. Nucleotide composition revealed an AT bias, accounting for 77.5% of total mt genome nucleotides. Phylogenetic analyses of 56 species/taxa of dipterans indicated that L. c. dorsalis and L. sericata are the closest among all sibling species of the genus Lucilia, which helps to explain species evolution within the family Luciliinae. This study provides the first complete mt genome sequence for L. c. dorsalis derived from the NT, Australia to facilitate species identification and the examination of the evolutionary history of these blowflies.

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.

A phenotypic screen of the Global Health Priority Box identifies an insecticide with anthelmintic activity.

BACKGROUND: Infection with parasitic nematodes (helminths), particularly those of the order Strongylida (such as Haemonchus contortus), can cause significant and burdensome diseases in humans and animals. Widespread drug (anthelmintic) resistance in livestock parasites, the absence of vaccines against most of these nematodes, and a lack of new and effective chemical entities on the commercial market demands the discovery of new anthelmintics. In the present study, we searched the Global Health Priority Box (Medicines for Malaria Venture) for new candidates for anthelmintic development. METHODS: We employed a whole-organism, motility-based phenotypic screening assay to identify compounds from the Global Health Priority Box with activity against larvae of the model parasite H. contortus, and the free-living comparator nematode Caenorhabditis elegans. Hit compounds were further validated via dose-response assays, with lead candidates then assessed for nematocidal activity against H. contortus adult worms, and additionally, for cytotoxic and mitotoxic effects on human hepatoma (HepG2) cells. RESULTS: The primary screen against H. contortus and C. elegans revealed or reidentified 16 hit compounds; further validation established MMV1794206, otherwise known as 'flufenerim', as a significant inhibitor of H. contortus larval motility (half-maximal inhibitory concentration [IC50] = 18 µM) and development (IC50 = 1.2 µM), H. contortus adult female motility (100% after 12 h of incubation) and C. elegans larval motility (IC50 = 0.22 µM). Further testing on a mammalian cell line (human hepatoma HepG2 cells), however, identified flufenerim to be both cytotoxic (half-maximal cytotoxic concentration [CC50] < 0.7 µM) and mitotoxic (half-maximal mitotoxic concentration [MC50] < 0.7 µM). CONCLUSIONS: The in vitro efficacy of MMV1794206 against the most pathogenic stages of H. contortus, as well as the free-living C. elegans, suggests the potential for development as a broad-spectrum anthelmintic compound; however, the high toxicity towards mammalian cells presents a significant hindrance. Further work should seek to establish the protein-drug interactions of MMV1794206 in a nematode model, to unravel the mechanism of action, in addition to an advanced structure-activity relationship investigation to optimise anthelmintic activity and eliminate mammalian cell toxicity.

Comparative structure activity and target exploration of 1,2-diphenylethynes in Haemonchus contortus and Caenorhabditis elegans.

Infections and diseases caused by parasitic nematodes have a major adverse impact on the health and productivity of animals and humans worldwide. The control of these parasites often relies heavily on the treatment with commercially available chemical compounds (anthelmintics). However, the excessive or uncontrolled use of these compounds in livestock animals has led to major challenges linked to drug resistance in nematodes. Therefore, there is a need to develop new anthelmintics with novel mechanism(s) of action. Recently, we identified a small molecule, designated UMW-9729, with nematocidal activity against the free-living model organism Caenorhabditis elegans. Here, we evaluated UMW-9729's potential as an anthelmintic in a structure-activity relationship (SAR) study in C. elegans and the highly pathogenic, blood-feeding Haemonchus contortus (barber's pole worm), and explored the compound-target relationship using thermal proteome profiling (TPP). First, we synthesised and tested 25 analogues of UMW-9729 for their nematocidal activity in both H. contortus (larvae and adults) and C. elegans (young adults), establishing a preliminary nematocidal pharmacophore for both species. We identified several compounds with marked activity against either H. contortus or C. elegans which had greater efficacy than UMW-9729, and found a significant divergence in compound bioactivity between these two nematode species. We also identified a UMW-9729 analogue, designated 25, that moderately inhibited the motility of adult female H. contortus in vitro. Subsequently, we inferred three H. contortus proteins (HCON_00134350, HCON_00021470 and HCON_00099760) and five C. elegans proteins (F30A10.9, F15B9.8, B0361.6, DNC-4 and UNC-11) that interacted directly with UMW-9729; however, no conserved protein target was shared between the two nematode species. Future work aims to extend the SAR investigation in these and other parasitic nematode species, and validate individual proteins identified here as possible targets of UMW-9729. Overall, the present study evaluates this anthelmintic candidate and highlights some challenges associated with early anthelmintic investigation.

Ocular Dirofilariasis in Migrant from Sri Lanka, Australia.

We describe a case of imported ocular dirofilariasis in Australia, linked to the Hong Kong genotype of Dirofilaria sp., in a migrant from Sri Lanka. Surgical extraction and mitochondrial sequences analyses confirmed this filarioid nematode as the causative agent and a Dirofilaria sp. not previously reported in Australia.

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.

Marked genetic diversity within Blastocystis in Australian wildlife revealed using a next generation sequencing-phylogenetic approach.

Blastocystis is a genus of intestinal stramenopiles that infect vertebrates, and may cause disease of the alimentary tract. Currently, at least 40 genotypes ("subtypes") of Blastocystis are recognised worldwide based on sequence data for the small subunit of the nuclear ribosomal RNA (SSU-rRNA) gene. Despite the numerous studies of Blastocystis worldwide, very few studies have explored Blastocystis in wild animals, particularly in Australia. Here, we used a PCR-based next generation sequencing (NGS)-phylogenetic approach to genetically characterise and classify Blastocystis variants from selected wildlife in the Australian state of Victoria. In total, 1658 faecal samples were collected from nine host species, including eastern grey kangaroo, swamp wallaby, common wombat, deer, European rabbit, canines and emu. Genomic DNA was extracted from these samples, a 500 bp region of the SSU-rRNA gene amplified by polymerase chain reaction (PCR) and, then, a subset of samples sequenced using Illumina technology. Primary PCR detected Blastocystis in 482 of the 1658 samples (29%), with the highest percentage in fallow deer (63%). Subsequent, Illumina-based sequencing of a subset of 356 samples revealed 55 distinct amplicon sequence variants (ASVs) representing seven currently-recognised subtypes (STs) [ST13 (prominent in marsupials), ST10, ST14, ST21, ST23, ST24 and ST25 (prominent in deer)] and two novel STs (ST45 and ST46) in marsupials. Mixed infections of different STs were observed in macropods, deer, emu and canids (fox, feral dog or dingo), but no infection was detected in rabbits or wombats. This study reveals marked genetic diversity within Blastocystis in a small number of species of wild animals in Australia, suggesting complexity in the genetic composition and transmission patterns of members of the genus Blastocystis in this country.

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.

DeepSecE: A Deep-Learning-Based Framework for Multiclass Prediction of Secreted Proteins in Gram-Negative Bacteria.

Proteins secreted by Gram-negative bacteria are tightly linked to the virulence and adaptability of these microbes to environmental changes. Accurate identification of such secreted proteins can facilitate the investigations of infections and diseases caused by these bacterial pathogens. However, current bioinformatic methods for predicting bacterial secreted substrate proteins have limited computational efficiency and application scope on a genome-wide scale. Here, we propose a novel deep-learning-based framework-DeepSecE-for the simultaneous inference of multiple distinct groups of secreted proteins produced by Gram-negative bacteria. DeepSecE remarkably improves their classification from nonsecreted proteins using a pretrained protein language model and transformer, achieving a macro-average accuracy of 0.883 on 5-fold cross-validation. Performance benchmarking suggests that DeepSecE achieves competitive performance with the state-of-the-art binary predictors specialized for individual types of secreted substrates. The attention mechanism corroborates salient patterns and motifs at the N or C termini of the protein sequences. Using this pipeline, we further investigate the genome-wide prediction of novel secreted proteins and their taxonomic distribution across ~1,000 Gram-negative bacterial genomes. The present analysis demonstrates that DeepSecE has major potential for the discovery of disease-associated secreted proteins in a diverse range of Gram-negative bacteria. An online web server of DeepSecE is also publicly available to predict and explore various secreted substrate proteins via the input of bacterial genome sequences.

Human Neural Larva Migrans Caused by Ophidascaris robertsi Ascarid.

We describe a case in Australia of human neural larva migrans caused by the ascarid Ophidascaris robertsi, for which Australian carpet pythons are definitive hosts. We made the diagnosis after a live nematode was removed from the brain of a 64-year-old woman who was immunosuppressed for a hypereosinophilic syndrome diagnosed 12 months earlier.

Mitochondrial genomic investigation reveals a clear association between species and genotypes of Lucilia and geographic origin in Australia.

BACKGROUND: Lucilia cuprina and L. sericata (family Calliphoridae) are globally significant ectoparasites of sheep. Current literature suggests that only one of these blowfly subspecies, L. cuprina dorsalis, is a primary parasite causing myiasis (flystrike) in sheep in Australia. These species and subspecies are difficult to distinguish using morphological features. Hence, being able to accurately identify blowflies is critical for diagnosis and for understanding their relationships with their hosts and environment. METHODS: In this study, adult blowflies (5 pools of 17 flies; n = 85) were collected from five locations in different states [New South Wales (NSW), Queensland (QLD), Tasmania (TAS), Victoria (VIC) and Western Australia (WA)] of Australia and their mitochondrial (mt) genomes were assembled. RESULTS: Each mt genome assembled was ~ 15 kb in size and encoded 13 protein-coding genes, 2 ribosomal RNAs, 22 transfer RNAs and a control region. The Lucilia species mt genomes were conserved in structure, and the genes retained the same order and direction. The overall nucleotide composition was heavily biased towards As and Ts-77.7% of the whole genomes. Pairwise nucleotide diversity suggested divergence between Lucilia cuprina cuprina, L. c. dorsalis and L. sericata. Comparative analyses of these mt genomes with published data demonstrated that the blowflies collected from sheep farm in TAS clustered within a clade with L. sericata. The flies collected from an urban location in QLD were more closely related to L. sericata and represented the subspecies L. c. cuprina, whereas the flies collected from sheep farms in NSW, VIC and WA represented the subspecies L. c. dorsalis. CONCLUSIONS: Phylogenetic analyses of the mt genomes representing Lucilia from the five geographic locations in Australia supported the previously demonstrated paraphyly of L. cuprina with respect to L. sericata and revealed that L. c. cuprina is distinct from L. c. dorsalis and that L. c. cuprina is more closely related to L. sericata than L. c. dorsalis. The mt genomes reported here provide an important molecular resource to develop tools for species- and subspecies-level identification of Lucilia from different geographical regions across Australia.

Genome-Wide Analysis of Haemonchus contortus Proteases and Protease Inhibitors Using Advanced Informatics Provides Insights into Parasite Biology and Host-Parasite Interactions.

Biodiversity within the animal kingdom is associated with extensive molecular diversity. The expansion of genomic, transcriptomic and proteomic data sets for invertebrate groups and species with unique biological traits necessitates reliable in silico tools for the accurate identification and annotation of molecules and molecular groups. However, conventional tools are inadequate for lesser-known organismal groups, such as eukaryotic pathogens (parasites), so that improved approaches are urgently needed. Here, we established a combined sequence- and structure-based workflow system to harness well-curated publicly available data sets and resources to identify, classify and annotate proteases and protease inhibitors of a highly pathogenic parasitic roundworm (nematode) of global relevance, called Haemonchus contortus (barber's pole worm). This workflow performed markedly better than conventional, sequence-based classification and annotation alone and allowed the first genome-wide characterisation of protease and protease inhibitor genes and gene products in this worm. In total, we identified 790 genes encoding 860 proteases and protease inhibitors representing 83 gene families. The proteins inferred included 280 metallo-, 145 cysteine, 142 serine, 121 aspartic and 81 "mixed" proteases as well as 91 protease inhibitors, all of which had marked physicochemical diversity and inferred involvements in >400 biological processes or pathways. A detailed investigation revealed a remarkable expansion of some protease or inhibitor gene families, which are likely linked to parasitism (e.g., host-parasite interactions, immunomodulation and blood-feeding) and exhibit stage- or sex-specific transcription profiles. This investigation provides a solid foundation for detailed explorations of the structures and functions of proteases and protease inhibitors of H. contortus and related nematodes, and it could assist in the discovery of new drug or vaccine targets against infections or diseases.

The Proteome and Lipidome of Extracellular Vesicles from Haemonchus contortus to Underpin Explorations of Host-Parasite Cross-Talk.

Many parasitic worms have a major adverse impact on human and animal populations worldwide due to the chronicity of their infections. There is a growing body of evidence indicating that extracellular vesicles (EVs) are intimately involved in modulating (suppressing) inflammatory/immune host responses and parasitism. As one of the most pathogenic nematodes of livestock animals, Haemonchus contortus is an ideal model system for EV exploration. Here, employing a multi-step enrichment process (in vitro culture, followed by ultracentrifugation, size exclusion and filtration), we enriched EVs from H. contortus and undertook the first comprehensive (qualitative and quantitative) multi-omic investigation of EV proteins and lipids using advanced liquid chromatography-mass spectrometry and informatics methods. We identified and quantified 561 proteins and 446 lipids in EVs and compared these molecules with those of adult worms. We identified unique molecules in EVs, such as proteins linked to lipid transportation and lipid species (i.e., sphingolipids) associated with signalling, indicating the involvement of these molecules in parasite-host cross-talk. This work provides a solid starting point to explore the functional roles of EV-specific proteins and lipids in modulating parasite-host cross-talk, and the prospect of finding ways of disrupting or interrupting this relationship to suppress or eliminate parasite infection.

Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins.

The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that are poorly characterised because of their highly repetitive nature and high GC content. Some of these genes encode proteins of the PE/PPE family, which are thought to be involved in host-pathogen interactions, virulence, and disease pathogenicity. Members of this family are genetically divergent and challenging to both identify and classify using conventional computational tools. Thus, advanced in silico methods are needed to identify proteins of this family for subsequent functional annotation efficiently. In this study, we developed the first deep learning-based approach, termed Digerati, for the rapid and accurate identification of PE and PPE family proteins. Digerati was built upon a multipath parallel hybrid deep learning framework, which equips multi-layer convolutional neural networks with bidirectional, long short-term memory, equipped with a self-attention module to effectively learn the higher-order feature representations of PE/PPE proteins. Empirical studies demonstrated that Digerati achieved a significantly better performance (∼18-20%) than alignment-based approaches, including BLASTP, PHMMER, and HHsuite, in both prediction accuracy and speed. Digerati is anticipated to facilitate community-wide efforts to conduct high-throughput identification and analysis of PE/PPE family members. The webserver and source codes of Digerati are publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/Digerati/.

Strongyloides coinfection in COVID-19 patients treated with corticosteroids: A systematic review.

The COVID-19 pandemic linked to the virus SARS-CoV-2, which began in China, affected ∼765 million people as of 30 April 2023. The widespread use of corticosteroids for the symptomatic treatment of COVID-19 could lead to the reactivation of infections of opportunistic pathogens, including Strongyloides. We sought to determine the clinical symptoms and demographic characteristics of SARS-CoV-2-Strongyloides co-infection, particularly in patients with severe disease and being treated with immunosuppressive drugs. To do this, we undertook a systematic review of the literature, and searched public accessible scientific databases-the Web of Science, Scopus, PubMed/Medline and Embase -for eligible studies (1 December 2019 to 30 August 2022). The review protocol is registered in PROSPERO (CRD42022377062). Descriptive statistical analyses were used to present the clinical and laboratory parameters of the co-infection; for this, we calculated prevalence using the following formula: positive cases/total number of cases × 100. Of a total of 593 studies identified, 17 studies reporting 26 co-infected patients met the criteria for inclusion in this review. The median age of these patients was 55.14 years. Most of cases (53.8%) were treated with dexamethasone, followed by methylprednisolone (26.9%). Eighteen of 26 patients were immigrants living in European countries or the USA; most of these immigrants originated from Latin America (58%) and South-East Asia (11%). The commonest symptoms of co-infection were abdominal pain (50%), fever (46.1%), dyspnoea (30.7%) and cough (30.7%), and frequently reported laboratory findings were high absolute eosinophil count (38.4%), high white blood cell count (30.7%), high C-reactive protein (23.0%) and high neutrophil count (19.2%). Two of the 26 patients (7.7%) had fatal outcomes. Most of the SARS-CoV-2-Strongyloides coinfected cases were immigrants living in developed countries, emphasising the need for clinicians in these countries to be aware of clinical and laboratory parameters associated with such co-infections, as well as the key importance of rapid and accurate diagnostic tests for timely and effective diagnosis and patient management.

Zoonotic helminths of dogs and risk factors associated with polyparasitism in Grenada, West Indies.

Canine soil-transmitted helminths (STHs) cause important zoonoses in the tropics, with varying degrees of intensity of infection in humans and dogs. This study aimed to investigate the prevalence and associated risk factors for STHs in community dogs residing in Grenada, West Indies. In May 2021, 232 canine fecal samples were examined for zoonotic helminths by microscopy (following flotation), and genomic DNA from a subset of 211 of these samples were subjected to multiplex qPCR for the detection and specific identification of hookworms, Toxocara spp. and Strongyloides. Microscopic examination revealed that 46.5% (108/232, 95% CI 40­52.9), 9% (21/232, 95% CI 5.35­12.7) and 5.2% (12/232, 95% CI 2.3­8) of the samples contained eggs of Ancylostoma spp., Toxocara spp. and Trichuris vulpis, respectively. Multiplex qPCR revealed that, 42.2% (89/211, 95% CI 35.5­48.8) were positive for at least 1 zoonotic parasite. Of these, 40.8% (86/211, 95% CI 34.1­47.3) of samples tested positive for Ancylostoma spp., 36% (76/211, 95% CI 29.5­42.9) were positive for A. caninum, 13.3% (28/211, 95% CI 9­18.6) for A. ceylanicum, 5.7% for T. canis (12/211, 95% CI 2.97­8.81) and 1% (2/211, 95% CI 0­2.26) for Strongyloides spp. (identified as S. stercoralis and S. papillosus by conventional PCR-based Sanger sequencing). Using a multiple logistic regression model, a low body score and free-roaming behaviour were significant predictors of test-positivity for these parasitic nematodes in dogs (P < 0.05). Further studies of zoonotic STHs in humans should help elucidate the public health relevance of these parasites in Grenada.

An informatic workflow for the enhanced annotation of excretory/secretory proteins of Haemonchus contortus.

Major advances in genomic and associated technologies have demanded reliable bioinformatic tools and workflows for the annotation of genes and their products via comparative analyses using well-curated reference data sets, accessible in public repositories. However, the accurate in silico annotation of molecules (proteins) encoded in organisms (e.g., multicellular parasites) which are evolutionarily distant from those for which these extensive reference data sets are available, including invertebrate model organisms (e.g., Caenorhabditis elegans - free-living nematode, and Drosophila melanogaster - the vinegar fly) and vertebrate species (e.g., Homo sapiens and Mus musculus), remains a major challenge. Here, we constructed an informatic workflow for the enhanced annotation of biologically-important, excretory/secretory (ES) proteins ("secretome") encoded in the genome of a parasitic roundworm, called Haemonchus contortus (commonly known as the barber's pole worm). We critically evaluated the performance of five distinct methods, refined some of them, and then combined the use of all five methods to comprehensively annotate ES proteins, according to gene ontology, biological pathways and/or metabolic (enzymatic) processes. Then, using optimised parameter settings, we applied this workflow to comprehensively annotate 2591 of all 3353 proteins (77.3%) in the secretome of H. contortus. This result is a substantial improvement (10-25%) over previous annotations using individual, "off-the-shelf" algorithms and default settings, indicating the ready applicability of the present, refined workflow to gene/protein sequence data sets from a wide range of organisms in the Tree-of-Life.

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.