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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

On the application of reverse vaccinology to parasitic diseases: a perspective on feature selection and ranking of vaccine candidates.

Reverse vaccinology has the potential to rapidly advance vaccine development against parasites, but it is unclear which features studied in silico will advance vaccine development. Here we consider Neospora caninum which is a globally distributed protozoan parasite causing significant economic and reproductive loss to cattle industries worldwide. The aim of this study was to use a reverse vaccinology approach to compile a worthy vaccine candidate list for N. caninum, including proteins containing pathogen-associated molecular patterns to act as vaccine carriers. The in silico approach essentially involved collecting a wide range of gene and protein features from public databases or computationally predicting those for every known Neospora protein. This data collection was then analysed using an automated high-throughput process to identify candidates. The final vaccine list compiled was judged to be the optimum within the constraints of available data, current knowledge, and existing bioinformatics programs. We consider and provide some suggestions and experience on how ranking of vaccine candidate lists can be performed. This study is therefore important in that it provides a valuable resource for establishing new directions in vaccine research against neosporosis and other parasitic diseases of economic and medical importance.

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.

The Transcriptome Sequence of Dientamoeba fragilis Offers New Biological Insights on its Metabolism, Kinome, Degradome and Potential Mechanisms of Pathogenicity.

Dientamoeba fragilis is a human bowel parasite with a worldwide distribution. Dientamoeba was once described as a rare and harmless commensal though recent reports suggest it is common and potentially pathogenic. Molecular data on Dientamoeba is scarce which limits our understanding of this parasite. To address this, sequencing of the Dientamoeba transcriptome was performed. Messenger RNA was extracted from cultured Dientamoeba trophozoites originating from clinical stool specimens, and sequenced using Roche GS FLX and Illumina HiSeq technologies. In total 6,595 Dientamoeba transcripts were identified. These sequences were analysed using the BLAST2GO software suite and via BLAST comparisons to sequences available from TrichDB, GenBank, MEROPS and kinase.com. Several novel KEGG pathway maps were generated and gene ontology analysis was also performed. These results are thoroughly discussed guided by knowledge available for other related protozoa. Attention is paid to the novel biological insights afforded by this data including peptidases and kinases of Dientamoeba, as well as its metabolism, novel chemotherapeutics and possible mechanisms of pathogenicity. Currently, this work represents the largest contribution to our understanding of Dientamoeba molecular biology and also represents a major contribution to our understanding of the trichomonads generally, many of which are important pathogens of humans and animals.

Improving the gene structure annotation of the apicomplexan parasite Neospora caninum fulfils a vital requirement towards an in silico-derived vaccine.

Neospora caninum is an apicomplexan parasite which can cause abortion in cattle, instigating major economic burden. Vaccination has been proposed as the most cost-effective control measure to alleviate this burden. Consequently the overriding aspiration for N. caninum research is the identification and subsequent evaluation of vaccine candidates in animal models. To save time, cost and effort, it is now feasible to use an in silico approach for vaccine candidate prediction. Precise protein sequences, derived from the correct open reading frame, are paramount and arguably the most important factor determining the success or failure of this approach. The challenge is that publicly available N. caninum sequences are mostly derived from gene predictions. Annotated inaccuracies can lead to erroneously predicted vaccine candidates by bioinformatics programs. This study evaluates the current N. caninum annotation for potential inaccuracies. Comparisons with annotation from a closely related pathogen, Toxoplasma gondii, are also made to distinguish patterns of inconsistency. More importantly, a mRNA sequencing (RNA-Seq) experiment is used to validate the annotation. Potential discrepancies originating from a questionable start codon context and exon boundaries were identified in 1943 protein coding sequences. We conclude, where experimental data were available, that the majority of N. caninum gene sequences were reliably predicted. Nevertheless, almost 28% of genes were identified as questionable. Given the limitations of RNA-Seq, the intention of this study was not to replace the existing annotation but to support or oppose particular aspects of it. Ideally, many studies aimed at improving the annotation are required to build a consensus. We believe this study, in providing a new resource on gene structure and annotation, is a worthy contributor to this endeavour.

Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores.

Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein's potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response-one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine discovery pool of evidence.