Commensal protist Tritrichomonas musculus exhibits a dynamic life cycle that induces extensive remodeling of the gut microbiota.

Commensal protists and gut bacterial communities exhibit complex relationships, mediated at least in part through host immunity. To improve our understanding of this tripartite interplay, we investigated community and functional dynamics between the murine protist Tritrichomonas musculus and intestinal bacteria in healthy and B-cell-deficient mice. We identified dramatic, protist-driven remodeling of resident microbiome growth and activities, in parallel with Tritrichomonas musculus functional changes, which were accelerated in the absence of B cells. Metatranscriptomic data revealed nutrient-based competition between bacteria and the protist. Single-cell transcriptomics identified distinct Tritrichomonas musculus life stages, providing new evidence for trichomonad sexual replication and the formation of pseudocysts. Unique cell states were validated in situ through microscopy and flow cytometry. Our results reveal complex microbial dynamics during the establishment of a commensal protist in the gut, and provide valuable data sets to drive future mechanistic studies.

MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities.

BACKGROUND: Whole microbiome RNASeq (metatranscriptomics) has emerged as a powerful technology to functionally interrogate microbial communities. A key challenge is how best to process, analyze, and interpret these complex datasets. In a typical application, a single metatranscriptomic dataset may comprise from tens to hundreds of millions of sequence reads. These reads must first be processed and filtered for low quality and potential contaminants, before being annotated with taxonomic and functional labels and subsequently collated to generate global bacterial gene expression profiles. RESULTS: Here, we present MetaPro, a flexible, massively scalable metatranscriptomic data analysis pipeline that is cross-platform compatible through its implementation within a Docker framework. MetaPro starts with raw sequence read input (single-end or paired-end reads) and processes them through a tiered series of filtering, assembly, and annotation steps. In addition to yielding a final list of bacterial genes and their relative expression, MetaPro delivers a taxonomic breakdown based on the consensus of complementary prediction algorithms, together with a focused breakdown of enzymes, readily visualized through the Cytoscape network visualization tool. We benchmark the performance of MetaPro against two current state-of-the-art pipelines and demonstrate improved performance and functionality. CONCLUSIONS: MetaPro represents an effective integrated solution for the processing and analysis of metatranscriptomic datasets. Its modular architecture allows new algorithms to be deployed as they are developed, ensuring its longevity. To aid user uptake of the pipeline, MetaPro, together with an established tutorial that has been developed for educational purposes, is made freely available at https://github.com/ParkinsonLab/MetaPro . The software is freely available under the GNU general public license v3. Video Abstract.

The commensal protist Tritrichomonas musculus exhibits a dynamic life cycle that induces extensive remodeling of the gut microbiota.

Commensal protists and gut bacterial communities exhibit complex relationships, mediated at least in part through host immunity. To improve our understanding of this tripartite interplay, we investigated community and functional dynamics between the murine protist Tritrichomonas musculus ( T. mu ) and intestinal bacteria in healthy and B cell-deficient mice. We identified dramatic, protist-driven remodeling of resident microbiome growth and activities, in parallel with T. mu functional changes, accelerated in the absence of B cells. Metatranscriptomic data revealed nutrient-based competition between bacteria and the protist. Single cell transcriptomics identified distinct T. mu life stages, providing new evidence for trichomonad sexual replication and the formation of pseudocysts. Unique cell states were validated in situ through microscopy and flow cytometry. Our results reveal complex microbial dynamics during the establishment of a commensal protist in the gut, and provide valuable datasets to drive future mechanistic studies.

Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation.

Constraint-based modeling is a powerful framework for studying cellular metabolism, with applications ranging from predicting growth rates and optimizing production of high value metabolites to identifying enzymes in pathogens that may be targeted for therapeutic interventions. Results from modeling experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools, notably with higher precision and recall on the eukaryote C. elegans and when compared to UniProt annotations in two bacterial species. Code for Architect is available at https://github.com/ParkinsonLab/Architect. For ease-of-use, Architect can be readily set up and utilized using its Docker image, maintained on Docker Hub.

Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets.

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia-present in many filariae-which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

Improved enzyme annotation with EC-specific cutoffs using DETECT v2.

Summary: We present DETECT v2-an enzyme annotation tool which considers the effect of sequence diversity when assigning enzymatic function [as an Enzyme Commission (EC) number] to a protein sequence. In addition to capturing more enzyme classes than the previous version, we now provide EC-specific cutoffs that greatly increase precision and recall of assignments and show its performance in the context of pathways. Availability and implementation: https://github.com/ParkinsonLab/DETECT-v2. Supplementary information: Supplementary data are available at Bioinformatics online.

The genome of Onchocerca volvulus, agent of river blindness.

Human onchocerciasis is a serious neglected tropical disease caused by the filarial nematode Onchocerca volvulus that can lead to blindness and chronic disability. Control of the disease relies largely on mass administration of a single drug, and the development of new drugs and vaccines depends on a better knowledge of parasite biology. Here, we describe the chromosomes of O. volvulus and its Wolbachia endosymbiont. We provide the highest-quality sequence assembly for any parasitic nematode to date, giving a glimpse into the evolution of filarial parasite chromosomes and proteomes. This resource was used to investigate gene families with key functions that could be potentially exploited as targets for future drugs. Using metabolic reconstruction of the nematode and its endosymbiont, we identified enzymes that are likely to be essential for O. volvulus viability. In addition, we have generated a list of proteins that could be targeted by Federal-Drug-Agency-approved but repurposed drugs, providing starting points for anti-onchocerciasis drug development.

Systems-based analysis of the Sarcocystis neurona genome identifies pathways that contribute to a heteroxenous life cycle.

UNLABELLED: Sarcocystis neurona is a member of the coccidia, a clade of single-celled parasites of medical and veterinary importance including Eimeria, Sarcocystis, Neospora, and Toxoplasma. Unlike Eimeria, a single-host enteric pathogen, Sarcocystis, Neospora, and Toxoplasma are two-host parasites that infect and produce infectious tissue cysts in a wide range of intermediate hosts. As a genus, Sarcocystis is one of the most successful protozoan parasites; all vertebrates, including birds, reptiles, fish, and mammals are hosts to at least one Sarcocystis species. Here we sequenced Sarcocystis neurona, the causal agent of fatal equine protozoal myeloencephalitis. The S. neurona genome is 127 Mbp, more than twice the size of other sequenced coccidian genomes. Comparative analyses identified conservation of the invasion machinery among the coccidia. However, many dense-granule and rhoptry kinase genes, responsible for altering host effector pathways in Toxoplasma and Neospora, are absent from S. neurona. Further, S. neurona has a divergent repertoire of SRS proteins, previously implicated in tissue cyst formation in Toxoplasma. Systems-based analyses identified a series of metabolic innovations, including the ability to exploit alternative sources of energy. Finally, we present an S. neurona model detailing conserved molecular innovations that promote the transition from a purely enteric lifestyle (Eimeria) to a heteroxenous parasite capable of infecting a wide range of intermediate hosts. IMPORTANCE: Sarcocystis neurona is a member of the coccidia, a clade of single-celled apicomplexan parasites responsible for major economic and health care burdens worldwide. A cousin of Plasmodium, Cryptosporidium, Theileria, and Eimeria, Sarcocystis is one of the most successful parasite genera; it is capable of infecting all vertebrates (fish, reptiles, birds, and mammals-including humans). The past decade has witnessed an increasing number of human outbreaks of clinical significance associated with acute sarcocystosis. Among Sarcocystis species, S. neurona has a wide host range and causes fatal encephalitis in horses, marine mammals, and several other mammals. To provide insights into the transition from a purely enteric parasite (e.g., Eimeria) to one that forms tissue cysts (Toxoplasma), we present the first genome sequence of S. neurona. Comparisons with other coccidian genomes highlight the molecular innovations that drive its distinct life cycle strategies.

Metabolic reconstruction identifies strain-specific regulation of virulence in Toxoplasma gondii.

Increasingly, metabolic potential is proving to be a critical determinant governing a pathogen's virulence as well as its capacity to expand its host range. To understand the potential contribution of metabolism to strain-specific infectivity differences, we present a constraint-based metabolic model of the opportunistic parasite, Toxoplasma gondii. Dominated by three clonal strains (Type I, II, and III demonstrating distinct virulence profiles), T. gondii exhibits a remarkably broad host range. Integrating functional genomic data, our model (which we term as iCS382) reveals that observed strain-specific differences in growth rates are driven by altered capacities for energy production. We further predict strain-specific differences in drug susceptibilities and validate one of these predictions in a drug-based assay, with a Type I strain demonstrating resistance to inhibitors that are effective against a Type II strain. We propose that these observed differences reflect an evolutionary strategy that allows the parasite to extend its host range, as well as result in a subsequent partitioning into discrete strains that display altered virulence profiles across different hosts, different organs, and even cell types.