Even though the biosphere is primarily microbial, most of the microbial world is still terra incognita. But that’s changing. Metagenomics and microbiomics – sequencing-based tallies of microbes sampled from the environment and humans, respectively — have seen an explosion of research and publicity in the last decade. However, they mostly focus on bacteria. Protists — a catch-all word, for the diverse array of single-celled eukaryotes found in virtually all moist environments — have received far less attention, despite being ubiquitous, ecologically important, and also medically significant.
For example, photosynthetic protists such as green algae are among the Earth’s main producers of oxygen and are the base of aquatic food chains. Their nonphotosynthetic counterparts such as amoeba and paramecium are fundamental to aquatic and terrestrial food chains as well as being predators of bacteria. They are also crucial decomposers of organic matter, both in free-living modes and as mutualistic inhabitants of certain insect guts where they break down cellulose. Some protist species have evolved parasitic and even pathogenic relationships with host plants and animals, with devastating public health and economic consequences. These include species causing scourges we know as malaria, diarrheal disease, sleeping sickness, and leishmaniasis, as well as less lethal but common and debilitating agents such as the intestinal parasite Giardia and the sexually transmitted parasite Trichomonas vaginalis. A number of pathogenic protists are also zoonotic, meaning they are transmissible from animals to humans – a trait with worrisome implications for public health.
One reason for the relative neglect of protists in ‘-omic’ surveys is that in contrast to work focused on bacteria, there are no standardized protocols and markers for specifically analyzing protist DNA in taxonomically diverse samples. In microbiome samples, protist sequences must be distinguished amidst overwhelming quantities of host and bacterial DNA. In metagenomic surveys of environmental samples, they similarly compete for attention with sequences from bacteria, virus, fungi, plants, and animals. One such environment is sewage, a potentially useful source for monitoring zoonotic protists circulating where humans live. But producing an accurate estimate of their diversity in raw sewage is a formidable challenge.
Sequencing Zoonotic Protists In Raw Sewage
In a recent paper, researchers at New York University’s Center for Genomics and Systems Biology took on this challenge. They developed and optimized a sequencing-based workflow to detect protists in sewage, focusing particularly on zoonotic species. First, they evaluated the utility of ‘universal’ PCR primers for two variable regions V4 and V9 of 18S ribosomal RNA – often used in studies of eukaryotic diversity – to identify protists in a known set of mixed taxa. To create this ‘mock community’ they amplified and sequenced V4 and V9 regions from 23 species of protist, fungus, and vertebrate, combined the sequences into one set and used the clustering algorithm of the QIIME microbial community analysis software package to blindly group them into OTUs (Operational Taxonomic Units) by similarity. They did this for V4 and V9 sequences separately for comparison, yielding 20 and 18 OTUs, respectively.
OTUs were then ‘named’ by matching their sequences to reference sequences in the public SILVA rRNA sequence database (which proved in need of curation to correct and improve its protist sequence coverage). The software-assigned names were then compared to the actual, known sources of the sequences in each OTU. All 20 V4 OTUs were correctly identified at the genus level, and all but one was correctly identified at the species level. Of the 18 V9 OTUs, 16 were correctly identified to the genus level, 11 to the species level. These results showed that both 18S rRNA regions were capable of resolving protists, often down to the species level, within a taxonomically wide-ranging mixture of sequences.
Maritz et al. then incorporated V4 and V9 primers into the development of an optimized pipeline for protist survey of raw sewage, using samples from the outflow of an apartment complex in New York City. The pipeline includes optimized steps for sample collection, DNA extraction, 18S rRNA PCR and V4 and V9 DNA library prep, Illumina deep sequencing, processing of Illumina reads, OTU generation, and taxonomic and community diversity analysis. Through trial and error, they found that same-day DNA extraction from 1 ml volumes of fresh, unfrozen sewage, combined with the use of a vertebrate blocking primer (which, surprisingly, also blocked invertebrate sequences) during the PCR amplification step, produced the best balance of DNA yield and range of detection of protists. Both V4 and V9 primer sets were recommended to be used on a sample, as their powers of detection overlapped and complemented each other.
Free-living bacteria-eating ciliates and flagellates were found to dominate the protist component of sewage, but DNA signatures of various parasitic, zoonotic, and potentially pathogenic protists were also detected at lower levels, including human gut-infecting protists from trichomonad (Pentatrichomonas), amoeboid (Entamoeba), apicomplexan (Cryptosporidium) and heterokont (Blastocystis) lineages, as well a bird trichomonad, and the feline/human parasite Toxoplasma.
It is true that many OTUs from raw sewage went unidentified, indicating gaps remaining in the updated 18S database, and by inference, in our sequence coverage of the microbial eukaryote world. Sequencing-based surveys can’t tell us if the organisms detected were alive or dead, and they are necessarily just ‘snapshots’ of populations that probably change frequently. But the methods used by Maritz et al. promise a viable way to track protist population trends over time in sewage – a potential boon to public health.
These findings are described in the article entitled An 18S rRNA Workflow for Characterizing Protists in Sewage, with Focus on Zoonotic Trichomonads, published in the journal Microbial Ecology. This work was led by Julia Maritz and Jane Carlton from New York University.