The three most common sexually transmitted infections (STIs) are Chlamydia trachomatis (CT), Neisseria gonorrhoeae (GC) and Trichomonas vaginalis (TV). The prevalence of these STIs in Taiwan remains largely unknown and the risk of STI acquisition affected by the vaginal microbiota is also elusive. In this study, a total of 327 vaginal swabs collected from women with vaginitis were analyzed to determine the presence of STIs and the associated microorganisms by using the BD Max CT/GC/TV molecular assay, microbial cultures, and 16S rRNA sequencing. The prevalence of CT, TV, and GC was 10.8%, 2.2% and 0.6%, respectively. A culture-dependent method identified that Escherichia coli and Streptococcus agalactiae (GBS) were more likely to be associated with CT and TV infections. In CT-positive patients, the vaginal microbiota was dominated by L. iners, and the relative abundance of Gardnerella vaginalis (12.46%) was also higher than that in TV-positive patients and the non-STIs group. However, Lactobacillus spp. was significantly lower in TV-positive patients, while GBS (10.11%), Prevotella bivia (6.19%), Sneathia sanguinegens (12.75%), and Gemella asaccharolytica (5.31%) were significantly enriched. Using an in vitro co-culture assay, we demonstrated that the growth of L. iners was suppressed in the initial interaction with TV, but it may adapt and survive after longer exposure to TV. Additionally, it is noteworthy that TV was able to promote GBS growth. Our study highlights the vaginal microbiota composition associated with the common STIs and the crosstalk between TV and the associated bacteria, paving the way for future development of health interventions targeting the specific vaginal bacterial taxa to reduce the risk of common STIs.

Millions of people throughout the world suffer from parasite infections. Traditionally, technicians use manual eye inspection of microscopic specimens to perform a parasite examination. However, manual operations have limitations that hinder the ability to obtain precise egg counts and cause inefficient identification of infected parasites on co-infections. The technician requirements for handling a large number of microscopic examinations in countries that have limited medical resources are substantial. We developed the helminth egg analysis platform (HEAP) as a user-friendly microscopic helminth eggs identification and quantification platform to assist medical technicians during parasite infection examination. Multiple deep learning strategies including SSD (Single Shot MultiBox Detector), U-net, and Faster R-CNN (Faster Region-based Convolutional Neural Network) are integrated to identify the same specimen allowing users to choose the best predictions. An image binning and egg-in-edge algorithm based on pixel density detection was developed to increase the performance. Computers with different operation systems can be gathered to lower the computation time using our easy-to-deploy software architecture. A user-friendly interface is provided to substantially increase the efficiency of manual validation. To adapt to low-cost computers, we architected a distributed computing structure with high flexibilities. HEAP serves not only as a prediction service provider but also as a parasitic egg database of microscopic helminth egg image collection, labeling data and pretrained models. All images and labeling resources are free and accessible at http://heap.cgu.edu.tw. HEAP can also be an ideal education and training resource for helminth egg examination.

Accumulated evidence suggests that the endosymbiotic Trichomonasvirus (TVV) may play a role in the pathogenesis and drug susceptibility of Trichomonas vaginalis. Several reports have shown that extracellular vesicles (EVs) released from TVV-positive (TVV+) trichomonads can modulate the immune response in human vaginal epithelial cells and animal models. These results prompted us to examine whether EVs released from TVV+ isolates contained TVV. We isolated small extracellular vesicles (sEVs) from six T. vaginalis isolates that were either TVV free (ATCC 50143), harbored a single (ATCC 30236, ATCC 30238, T1), two (ATCC PRA-98), or three TVV subspecies (ATCC 50148). The presence of TVV subspecies in the six isolates was observed using reverse transcription-polymerase chain reaction (RT-PCR). Transmission electron microscopy (TEM) confirmed the presence of cup-shaped sEVs with a size range from 30–150 nm. Trichomonas vaginalis tetraspanin (TvTSP1; TVAG_019180), the classical exosome marker, was identified in all the sEV preparations. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis showed that all the sEVs isolated from TVV+ isolates contain viral capsid proteins derived from the same TVV subspecies in that isolate as demonstrated by RT-PCR. To provide more comprehensive information on the TVV subspecies population in other T. vaginalis isolates, we investigated the distribution of TVV subspecies in twenty-four isolates by mining the New-Generation Sequencing (NGS) RNAseq datasets. Our results should be beneficial for future studies investigating the role of TVV on the pathogenicity of T. vaginalis and the possible transmission of virus subspecies among different isolates via sEVs.

Trichomonas vaginalis is parasitic protozoan that causes human urogenital infections. Accumulated reports indicated that exosomes released by this parasite play a crucial role in transmitting information and substances between cells during host-parasite interactions. Current knowledge on the protein contents in T. vaginalis exosome is mainly generated from three previous studies that used different T. vaginalis isolates as an experimental model. Whether T. vaginalis exosomes comprise a common set of proteins (core exosome proteome) is still unclear. To explore the core exosome proteome in T. vaginalis, we used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify the contents of sucrose ultracentrifugation-enriched exosome and supernatant fractions isolated from six isolates. Transmission electron microscopy (TEM) confirmed the presence of exosomes in the enriched fraction. Proteomic analysis identified a total of 1870 proteins from exosomal extracts. There were 1207 exosomal-specific proteins after excluding 436 'non-core exosomal proteins'. Among these, 72 common exosomal-specific proteins were expressed in all six isolates. Compared with three published T. vaginalis exosome proteome datasets, we identified 16 core exosomal-specific proteins. These core exosomal-specific proteins included tetraspanin (TvTSP1), the classical exosome marker, and proteins mainly involved in catalytic activity and binding such as ribosomal proteins, ras-associated binding (Rab) proteins, and heterotrimeric G proteins. Our study highlighted the importance of using supernatant fraction from exosomal extract as a control to eliminate 'non-core exosomal proteins'. We compiled a reference core exosome proteome of T. vaginalis, which is essential for developing a fundamental understanding of exosome-mediated cell communication and host-parasite interaction.

Although sequencing a human genome has become affordable, identifying genetic variants from whole-genome sequence data is still a hurdle for researchers without adequate computing equipment or bioinformatics support. GATK is a gold standard method for the identification of genetic variants and has been widely used in genome projects and population genetic studies for many years. This was until the Google Brain team developed a new method, DeepVariant, which utilizes deep neural networks to construct an image classification model to identify genetic variants. However, the superior accuracy of DeepVariant comes at the cost of computational intensity, largely constraining its applications. Accordingly, we present DeepVariant-on-Spark to optimize resource allocation, enable multi-GPU support, and accelerate the processing of the DeepVariant pipeline. To make DeepVariant-on-Spark more accessible to everyone, we have deployed the DeepVariant-on-Spark to the Google Cloud Platform (GCP). Users can deploy DeepVariant-on-Spark on the GCP following our instruction within 20 minutes and start to analyze at least ten whole-genome sequencing datasets using free credits provided by the GCP. DeepVaraint-on-Spark is freely available for small-scale genome analysis using a cloud-based computing framework, which is suitable for pilot testing or preliminary study, while reserving the flexibility and scalability for large-scale sequencing projects.

Trichomonas vaginalis is the causative agent of trichomoniasis, the most prevalent non-viral sexually transmitted infection worldwide. Metronidazole (MTZ) is the mainstay of anti-trichomonal chemotherapy; however, drug resistance has become an increasingly worrying issue. Additionally, the molecular events of MTZ-induced cell death in T. vaginalis remain elusive. To gain insight into the differential expression of genes related to MTZ resistance and cell death, we conducted RNA-sequencing of three paired MTZ-resistant (MTZ-R) and MTZ-sensitive (MTZ-S) T. vaginalis strains treated with or without MTZ. Comparative transcriptomes analysis identified that several putative drug-resistant genes were exclusively upregulated in different MTZ-R strains, such as ATP-binding cassette (ABC) transporters and multidrug resistance pumps. Additionally, several shared upregulated genes among all the MTZ-R transcriptomes were not previously identified in T. vaginalis, such as 5′-nucleotidase surE and Na⁺-driven multidrug efflux pump, which are a potential stress response protein and a multidrug and toxic compound extrusion (MATE)-like protein, respectively. Functional enrichment analysis revealed that purine and pyrimidine metabolisms were suppressed in MTZ-S parasites upon drug treatment, whereas the endoplasmic reticulum-associated degradation (ERAD) pathway, proteasome, and ubiquitin-mediated proteolysis were strikingly activated, highlighting the novel pathways responsible for drug-induced stress. Our work presents the most detailed analysis of the transcriptional changes and the regulatory networks associated with MTZ resistance and MTZ-induced signaling, providing insights into MTZ resistance and cell death mechanisms in trichomonads.