Observations on the transmission of Dientamoeba fragilis and the cyst life cycle stage.

Little is known about the life cycle and mode of transmission of Dientamoeba fragilis. Recently it was suggested that fecal­oral transmission of cysts may play a role in the transmission of D. fragilis. In order to establish an infection, D. fragilis is required to remain viable when exposed to the pH of the stomach. In this study, we investigated the ability of cultured trophozoites to withstand the extremes of pH. We provide evidence that trophozoites of D. fragilis are vulnerable to highly acidic conditions. We also investigated further the ultrastructure of D. fragilis cysts obtained from mice and rats by transmission electron microscopy. These studies of cysts showed a clear cyst wall surrounding an encysted parasite. The cyst wall was double layered with an outer fibrillar layer and an inner layer enclosing the parasite. Hydrogenosomes, endoplasmic reticulum and nuclei were present in the cysts. Pelta-axostyle structures, costa and axonemes were identifiable and internal flagellar axonemes were present. This study therefore provides additional novel details and knowledge of the ultrastructure of the cyst stage of D. fragilis.

30 years of parasitology research analysed by text mining.

Bibliometric methods were used to analyse the major research trends, themes and topics over the last 30 years in the parasitology discipline. The tools used were SciMAT, VOSviewer and SWIFT-Review in conjunction with the parasitology literature contained in the MEDLINE, Web of Science, Scopus and Dimensions databases. The analyses show that the major research themes are dynamic and continually changing with time, although some themes identified based on keywords such as malaria, nematode, epidemiology and phylogeny are consistently referenced over time. We note the major impact of countries like Brazil has had on the literature of parasitology research. The increase in recent times of research productivity on 'antiparasitics' is discussed, as well as the change in emphasis on different antiparasitic drugs and insecticides over time. In summary, innovation in parasitology is global, extensive, multidisciplinary, constantly evolving and closely aligned with the availability of technology.

Dientamoeba fragilis, the Neglected Trichomonad of the Human Bowel.

Dientamoeba fragilis is a protozoan parasite of the human bowel, commonly reported throughout the world in association with gastrointestinal symptoms. Despite its initial discovery over 100 years ago, arguably, we know less about this peculiar organism than any other pathogenic or potentially pathogenic protozoan that infects humans. The details of its life cycle and mode of transmission are not completely known, and its potential as a human pathogen is debated within the scientific community. Recently, several major advances have been made with respect to this organism's life cycle and molecular biology. While many questions remain unanswered, these and other recent advances have given rise to some intriguing new leads, which will pave the way for future research. This review encompasses a large body of knowledge generated on various aspects of D. fragilis over the last century, together with an update on the most recent developments. This includes an update on the latest diagnostic techniques and treatments, the clinical aspects of dientamoebiasis, the development of an animal model, the description of a D. fragilis cyst stage, and the sequencing of the first D. fragilis transcriptome.

A guide to in silico vaccine discovery for eukaryotic pathogens.

In this article, a framework for an in silico pipeline is presented as a guide to high-throughput vaccine candidate discovery for eukaryotic pathogens, such as helminths and protozoa. Eukaryotic pathogens are mostly parasitic and cause some of the most damaging and difficult to treat diseases in humans and livestock. Consequently, these parasitic pathogens have a significant impact on economy and human health. The pipeline is based on the principle of reverse vaccinology and is constructed from freely available bioinformatics programs. There are several successful applications of reverse vaccinology to the discovery of subunit vaccines against prokaryotic pathogens but not yet against eukaryotic pathogens. The overriding aim of the pipeline, which focuses on eukaryotic pathogens, is to generate through computational processes of elimination and evidence gathering a ranked list of proteins based on a scoring system. These proteins are either surface components of the target pathogen or are secreted by the pathogen and are of a type known to be antigenic. No perfect predictive method is yet available; therefore, the highest-scoring proteins from the list require laboratory validation.

Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology.

UNLABELLED: We present Vacceed, a highly configurable and scalable framework designed to automate the process of high-throughput in silico vaccine candidate discovery for eukaryotic pathogens. Given thousands of protein sequences from the target pathogen as input, the main output is a ranked list of protein candidates determined by a set of machine learning algorithms. Vacceed has the potential to save time and money by reducing the number of false candidates allocated for laboratory validation. Vacceed, if required, can also predict protein sequences from the pathogen's genome. AVAILABILITY AND IMPLEMENTATION: Vacceed is tested on Linux and can be freely downloaded from https://github.com/sgoodswe/vacceed/releases (includes a worked example with sample data). Vacceed User Guide can be obtained from https://github.com/sgoodswe/vacceed.

Control options for Neospora caninum--is there anything new or are we going backwards?

Recent work has highlighted and enumerated the economic annual losses due to Neospora caninum abortions worldwide, which should provide strong motivation for the control of bovine neosporosis. However, with the recent withdrawal from sale of the only commercially available vaccine, control options for N. caninum have become more restricted. While researchers continue to work on developing alternative efficacious vaccines, what are the control options presently available for the cattle industries? At the practical level, recommendations for 'Test-and-cull', or 'not breeding from seropositive dams' stand diametrically opposed to alternative options put forward that suggest a primary producer is better advised to keep those cows in the herd that are already seropositive, i.e., assumed to be chronically infected, and indeed those that have already aborted once. Treatment with a coccidiostat has been recommended as the only economically viable option, yet no such treatment has gained official, regulatory approval. Dogs are central to the life cycle of N. caninum and have repeatedly been associated with infection and abortions in cattle by epidemiological studies. Knowledge and understanding of that pivotal role should be able to be put to use in control programmes. The present review canvasses the relevant literature for evidence for control options for N. caninum (some of them proven, many not) and assesses them in the light of the authors' knowledge and experience with control of N. caninum.

A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms.

BACKGROUND: An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets. RESULTS: The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally. CONCLUSIONS: Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.

Twitter trends in #Parasitology determined by text mining and topic modelling.

This study investigated the emergence and use of Twitter, as of July 2023 being rebranded as X, as the main forum for social media communication in parasitology. A dataset of tweets was constructed using a keyword search of Twitter with the search terms 'malaria', 'Plasmodium', 'Leishmania', 'Trypanosoma', 'Toxoplasma' and 'Schistosoma' for the period from 2011 to 2020. Exploratory data analyses of tweet content were conducted, including language, usernames and hashtags. To identify parasitology topics of discussion, keywords and phrases were extracted using KeyBert and biterm topic modelling. The sentiment of tweets was analysed using VADER. The results show that the number of tweets including the keywords increased from 2011 (for malaria) and 2013 (for the others) to 2020, with the highest number of tweets being recorded in 2020. The maximum number of yearly tweets for Plasmodium, Leishmania, Toxoplasma, Trypanosoma and Schistosoma was recorded in 2020 (2804, 2161, 1570, 680 and 360 tweets, respectively). English was the most commonly used language for tweeting, although the percentage varied across the searches. In tweets mentioning Leishmania, only ∼37% were in English, with Spanish being more common. Across all the searches, Portuguese was another common language found. Popular tweets on Toxoplasma contained keywords relating to mental health including depression, anxiety and schizophrenia. The Trypanosoma tweets referenced drugs (benznidazole, nifurtimox) and vectors (bugs, triatomines, tsetse), while the Schistosoma tweets referenced areas of biology including pathology, eggs and snails. A wide variety of individuals and organisations were shown to be associated with Twitter activity. Many journals in the parasitology arena regularly tweet about publications from their journal, and professional societies promote activity and events that are important to them. These represent examples of trusted sources of information, often by experts in their fields. Social media activity of influencers, however, who have large numbers of followers, might have little or no training in science. The existence of such tweeters does raise cause for concern to parasitology, as one may start to question the quality of information being disseminated.

A guide to current methodology and usage of reverse vaccinology towards in silico vaccine discovery.

Reverse vaccinology (RV) was described at its inception in 2000 as an in silico process that starts from the genomic sequence of the pathogen and ends with a list of potential protein and/or peptide candidates to be experimentally validated for vaccine development. Twenty-two years later, this process has evolved from a few steps entailing a handful of bioinformatics tools to a multitude of steps with a plethora of tools. Other in silico related processes with overlapping workflow steps have also emerged with terms such as subtractive proteomics, computational vaccinology, and immunoinformatics. From the perspective of a new RV practitioner, determining the appropriate workflow steps and bioinformatics tools can be a time consuming and overwhelming task, given the number of choices. This review presents the current understanding of RV and its usage in the research community as determined by a comprehensive survey of scientific papers published in the last seven years. We believe the current mainstream workflow steps and tools presented here will be a valuable guideline for all researchers wanting to apply an up-to-date in silico vaccine discovery process.

A state-of-the-art methodology for high-throughput in silico vaccine discovery against protozoan parasites and exemplified with discovered candidates for Toxoplasma gondii.

Vaccine discovery against eukaryotic parasites is not trivial as highlighted by the limited number of known vaccines compared to the number of protozoal diseases that need one. Only three of 17 priority diseases have commercial vaccines. Live and attenuated vaccines have proved to be more effective than subunit vaccines but adversely pose more unacceptable risks. One promising approach for subunit vaccines is in silico vaccine discovery, which predicts protein vaccine candidates given thousands of target organism protein sequences. This approach, nonetheless, is an overarching concept with no standardised guidebook on implementation. No known subunit vaccines against protozoan parasites exist as a result of this approach, and consequently none to emulate. The study goal was to combine current in silico discovery knowledge specific to protozoan parasites and develop a workflow representing a state-of-the-art approach. This approach reflectively integrates a parasite's biology, a host's immune system defences, and importantly, bioinformatics programs needed to predict vaccine candidates. To demonstrate the workflow effectiveness, every Toxoplasma gondii protein was ranked in its capacity to provide long-term protective immunity. Although testing in animal models is required to validate these predictions, most of the top ranked candidates are supported by publications reinforcing our confidence in the approach.

Compilation of parasitic immunogenic proteins from 30 years of published research using machine learning and natural language processing.

The World Health Organisation reported in 2020 that six of the top 10 sources of death in low-income countries are parasites. Parasites are microorganisms in a relationship with a larger organism, the host. They acquire all benefits at the host's expense. A disease develops if the parasitic infection disrupts normal functioning of the host. This disruption can range from mild to severe, including death. Humans and livestock continue to be challenged by established and emerging infectious disease threats. Vaccination is the most efficient tool for preventing current and future threats. Immunogenic proteins sourced from the disease-causing parasite are worthwhile vaccine components (subunits) due to reliable safety and manufacturing capacity. Publications with 'subunit vaccine' in their title have accumulated to thousands over the last three decades. However, there are possibly thousands more reporting immunogenicity results without mentioning 'subunit' and/or 'vaccine'. The exact number is unclear given the non-standardised keywords in publications. The study aim is to identify parasite proteins that induce a protective response in an animal model as reported in the scientific literature within the last 30 years using machine learning and natural language processing. Source code to fulfil this aim and the vaccine candidate list obtained is made available.

Extensive production of Neospora caninum tissue cysts in a carnivorous marsupial succumbing to experimental neosporosis.

Experimental infections of Sminthopsis crassicaudata, the fat-tailed dunnart, a carnivorous marsupial widely distributed throughout the arid and semi-arid zones of Australia, show that this species can act as an intermediate host for Neospora caninum. In contrast to existing models that develop relatively few N. caninum tissue cysts, dunnarts offer a new animal model in which active neosporosis is dominated by tissue cyst production. The results provide evidence for a sylvatic life cycle of N. caninum in Australia between marsupials and wild dogs. It establishes the foundation for an investigation of the impact and costs of neosporosis to wildlife.

The ambiguous life of Dientamoeba fragilis: the need to investigate current hypotheses on transmission.

Dientamoeba fragilis is an inhabitant of the human bowel and is associated with gastrointestinal illness. Despite its discovery over a century ago, the details of Dientamoeba's life cycle are unclear and its mode of transmission is unknown. Several theories exist which attempt to explain how Dientamoeba may be transmitted. One theory suggests that animals are responsible for the transmission of Dientamoeba. However, reports of Dientamoeba in animals are sporadic and most are not supported by molecular evidence. Another theory suggests that Dientamoeba may be transmitted via the ova of a helminth. Given that the closest relative of Dientamoeba is transmitted via the ova of a helminth, this theory seems plausible. It has also been suggested that Dientamoeba could be transmitted directly between humans. This theory also seems plausible given that other relatives of Dientamoeba are transmitted in this way. Despite numerous investigations, Dientamoeba's mode of transmission remains unknown. This review discusses the strengths and weaknesses of theories relating to Dientamoeba's mode of transmission and, by doing so, indicates where gaps in current knowledge exist. Where information is lacking, suggestions are made as to how future research could improve our knowledge on the life cycle of Dientamoeba.

Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning.

Bovine babesiosis causes significant annual global economic loss in the beef and dairy cattle industry. It is a disease instigated from infection of red blood cells by haemoprotozoan parasites of the genus Babesia in the phylum Apicomplexa. Principal species are Babesia bovis, Babesia bigemina, and Babesia divergens. There is no subunit vaccine. Potential therapeutic targets against babesiosis include members of the exportome. This study investigates the novel use of protein secondary structure characteristics and machine learning algorithms to predict exportome membership probabilities. The premise of the approach is to detect characteristic differences that can help classify one protein type from another. Structural properties such as a protein's local conformational classification states, backbone torsion angles ϕ (phi) and ψ (psi), solvent-accessible surface area, contact number, and half-sphere exposure are explored here as potential distinguishing protein characteristics. The presented methods that exploit these structural properties via machine learning are shown to have the capacity to detect exportome from non-exportome Babesia bovis proteins with an 86-92% accuracy (based on 10-fold cross validation and independent testing). These methods are encapsulated in freely available Linux pipelines setup for automated, high-throughput processing. Furthermore, proposed therapeutic candidates for laboratory investigation are provided for B. bovis, B. bigemina, and two other haemoprotozoan species, Babesia canis, and Plasmodium falciparum.

Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed.

Bioinformatics programs have been developed that exploit informative signals encoded within protein sequences to predict protein characteristics. Unfortunately, there is no program as yet that can predict whether a protein will induce a protective immune response to a pathogen. Nonetheless, predicting those pathogen proteins most likely from those least likely to induce an immune response is feasible when collectively using predicted protein characteristics. Vacceed is a computational pipeline that manages different standalone bioinformatics programs to predict various protein characteristics, which offer supporting evidence on whether a protein is secreted or membrane -associated. A set of machine learning algorithms predicts the most likely pathogen proteins to induce an immune response given the supporting evidence. This chapter provides step by step descriptions of how to configure and operate Vacceed for a eukaryotic pathogen of the user's choice.

Applying Machine Learning to Predict the Exportome of Bovine and Canine Babesia Species That Cause Babesiosis.

Babesia infection of red blood cells can cause a severe disease called babesiosis in susceptible hosts. Bovine babesiosis causes global economic loss to the beef and dairy cattle industries, and canine babesiosis is considered a clinically significant disease. Potential therapeutic targets against bovine and canine babesiosis include members of the exportome, i.e., those proteins exported from the parasite into the host red blood cell. We developed three machine learning-derived methods (two novel and one adapted) to predict for every known Babesia bovis, Babesia bigemina, and Babesia canis protein the probability of being an exportome member. Two well-studied apicomplexan-related species, Plasmodium falciparum and Toxoplasma gondii, with extensive experimental evidence on their exportome or excreted/secreted proteins were used as important benchmarks for the three methods. Based on 10-fold cross validation and multiple train-validation-test splits of training data, we expect that over 90% of the predicted probabilities accurately provide a secretory or non-secretory indicator. Only laboratory testing can verify that predicted high exportome membership probabilities are creditable exportome indicators. However, the presented methods at least provide those proteins most worthy of laboratory validation and will ultimately save time and money.

The controversies surrounding Giardia intestinalis assemblages A and B.

Giardia intestinalis continues to be one of the most encountered parasitic diseases around the world. Although more frequently detected in developing countries, Giardia infections nonetheless pose significant public health problems in developed countries as well. Molecular characterisation of Giardia isolates from humans and animals reveals that there are two genetically different assemblages (known as assemblage A and B) that cause human infections. However, the current molecular assays used to genotype G. intestinalis isolates are quite controversial. This is in part due to a complex phenomenon where assemblages are incorrectly typed and underreported depending on which targeted locus is sequenced. In this review, we outline current knowledge based on molecular epidemiological studies and raise questions as to the reliability of current genotyping assays and a lack of a globally accepted method. Additionally, we discuss the clinical symptoms caused by G. intestinalis infection and how these symptoms vary depending on the assemblage infecting an individual. We also introduce the host-parasite factors that play a role in the subsequent clinical presentation of an infected person, and explore which assemblages are most seen globally.

Machine learning and applications in microbiology.

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

Research into Neospora caninum-What Have We Learnt in the Last Thirty Years?

BACKGROUND: Neospora caninum has been recognised world-wide, first as a disease of dogs, then as an important cause of abortions in cattle for the past thirty years. Over that time period, there have been improvements in the diagnosis of infection and abortion, new tests have been developed and validated, and it is timely to review progress to date. METHODS: Bibliometric methods were used to identify major trends and research topics present in the published literature on N. caninum. The tools used were SWIFT-Review, VOSviewer and SciMAT, along with the published papers found in the MEDLINE, Dimensions and Web of Science databases. A systematic review of the published Neospora literature (n = 2933) was also carried out via MEDLINE and systematically appraised for publications relevant to the pathogenesis, pathology and diagnosis of Neospora abortions. RESULTS: A total of 92 publications were included in the final analysis and grouped into four main time periods. In these four different time periods, the main research themes were "dogs", "abortion", "seroprevalence" and "infection". Diagnostics, including PCR, dominated the first two time periods, with an increased focus on transmission and abortions, and its risk factors in cattle. CONCLUSIONS: Longitudinal analyses indicated that the main themes were consistently investigated over the last 30 years through a wide range of studies, with evolving emphasis initially on dogs and diagnostic test development, followed by application to cattle, the identification of the risk factors leading to abortion, and in the latter time periods, an understanding of the immunity and a search for vaccines.

A Gene-Based Positive Selection Detection Approach to Identify Vaccine Candidates Using Toxoplasma gondii as a Test Case Protozoan Pathogen.

Over the last two decades, various in silico approaches have been developed and refined that attempt to identify protein and/or peptide vaccines candidates from informative signals encoded in protein sequences of a target pathogen. As to date, no signal has been identified that clearly indicates a protein will effectively contribute to a protective immune response in a host. The premise for this study is that proteins under positive selection from the immune system are more likely suitable vaccine candidates than proteins exposed to other selection pressures. Furthermore, our expectation is that protein sequence regions encoding major histocompatibility complexes (MHC) binding peptides will contain consecutive positive selection sites. Using freely available data and bioinformatic tools, we present a high-throughput approach through a pipeline that predicts positive selection sites, protein subcellular locations, and sequence locations of medium to high T-Cell MHC class I binding peptides. Positive selection sites are estimated from a sequence alignment by comparing rates of synonymous (dS) and non-synonymous (dN) substitutions among protein coding sequences of orthologous genes in a phylogeny. The main pipeline output is a list of protein vaccine candidates predicted to be naturally exposed to the immune system and containing sites under positive selection. Candidates are ranked with respect to the number of consecutive sites located on protein sequence regions encoding MHCI-binding peptides. Results are constrained by the reliability of prediction programs and quality of input data. Protein sequences from Toxoplasma gondii ME49 strain (TGME49) were used as a case study. Surface antigen (SAG), dense granules (GRA), microneme (MIC), and rhoptry (ROP) proteins are considered worthy T. gondii candidates. Given 8263 TGME49 protein sequences processed anonymously, the top 10 predicted candidates were all worthy candidates. In particular, the top ten included ROP5 and ROP18, which are T. gondii virulence determinants. The chance of randomly selecting a ROP protein was 0.2% given 8263 sequences. We conclude that the approach described is a valuable addition to other in silico approaches to identify vaccines candidates worthy of laboratory validation and could be adapted for other apicomplexan parasite species (with appropriate data).