Teresita M. Porter

Simultaneous assessment of the macrobiome and microbiome in a bulk sample of tropical arthropods through DNA metasystematics

Significance Ecological and evolutionary investigations require accurate and high-resolution biodiversity information. Conventional morphological approaches to identifying species in species-rich tropical ecosystems are often unavailable or incapable of timely, cost-effective identification. We show that next-generation sequencing (NGS) of cytochrome c oxidase subunit I (COI) DNA barcodes can accurately detect 83.5% of individually sequenced species (corresponding to 91% of individuals) in a bulk sample of terrestrial arthropods from a Costa Rican species-rich site. Additionally, the 16S and 18S ribosomal DNA gene regions obtained also provide an assessment of the bacteria and protozoa in the bulk sample. This metasystematic approach provides the initial infrastructure for a next generation of biodiversity assessment and environmental monitoring. It can lead to more effective understanding, appreciation, and management of complex ecosystems. Conventional assessments of ecosystem sample composition are based on morphology-based or DNA barcode identification of individuals. Both approaches are costly and time-consuming, especially when applied to the large number of specimens and taxa commonly included in ecological investigations. Next-generation sequencing approaches can overcome the bottleneck of individual specimen isolation and identification by simultaneously sequencing specimens of all taxa in a bulk mixture. Here we apply multiple parallel amplification primers, multiple DNA barcode markers, 454-pyrosequencing, and Illumina MiSeq sequencing to the same sample to maximize recovery of the arthropod macrobiome and the bacterial and other microbial microbiome of a bulk arthropod sample. We validate this method with a complex sample containing 1,066 morphologically distinguishable arthropods from a tropical terrestrial ecosystem with high taxonomic diversity. Multiamplicon next-generation DNA barcoding was able to recover sequences corresponding to 91% of the distinguishable individuals in a bulk environmental sample, as well as many species present as undistinguishable tissue. 454-pyrosequencing was able to recover 10 more families of arthropods and 30 more species than did conventional Sanger sequencing of each individual specimen. The use of other loci (16S and 18S ribosomal DNA gene regions) also added the detection of species of microbes associated with these terrestrial arthropods. This method greatly decreases the time and money necessary to perform DNA-based comparisons of biodiversity among ecosystem samples. This methodology opens the door to much cheaper and increased capacity for ecological and evolutionary studies applicable to a wide range of socio-economic issues, as well as a basic understanding of how the world works.

A Comprehensive, Automatically Updated Fungal ITS Sequence Dataset for Reference-Based Chimera Control in Environmental Sequencing Efforts

The nuclear ribosomal internal transcribed spacer (ITS) region is the most commonly chosen genetic marker for the molecular identification of fungi in environmental sequencing and molecular ecology studies. Several analytical issues complicate such efforts, one of which is the formation of chimeric—artificially joined—DNA sequences during PCR amplification or sequence assembly. Several software tools are currently available for chimera detection, but rely to various degrees on the presence of a chimera-free reference dataset for optimal performance. However, no such dataset is available for use with the fungal ITS region. This study introduces a comprehensive, automatically updated reference dataset for fungal ITS sequences based on the UNITE database for the molecular identification of fungi. This dataset supports chimera detection throughout the fungal kingdom and for full-length ITS sequences as well as partial (ITS1 or ITS2 only) datasets. The performance of the dataset on a large set of artificial chimeras was above 99.5%, and we subsequently used the dataset to remove nearly 1,000 compromised fungal ITS sequences from public circulation. The dataset is available at http://unite.ut.ee/repository.php and is subject to web-based third-party curation.

Massively parallel multiplex DNA sequencing for specimen identification using an Illumina MiSeq platform

Genetic information is a valuable component of biosystematics, especially specimen identification through the use of species-specific DNA barcodes. Although many genomics applications have shifted to High-Throughput Sequencing (HTS) or Next-Generation Sequencing (NGS) technologies, sample identification (e.g., via DNA barcoding) is still most often done with Sanger sequencing. Here, we present a scalable double dual-indexing approach using an Illumina Miseq platform to sequence DNA barcode markers. We achieved 97.3% success by using half of an Illumina Miseq flowcell to obtain 658 base pairs of the cytochrome c oxidase I DNA barcode in 1,010 specimens from eleven orders of arthropods. Our approach recovers a greater proportion of DNA barcode sequences from individuals than does conventional Sanger sequencing, while at the same time reducing both per specimen costs and labor time by nearly 80%. In addition, the use of HTS allows the recovery of multiple sequences per specimen, for deeper analysis of genetic variation in target gene regions.

Improving ITS sequence data for identification of plant pathogenic fungi

SummaryPlant pathogenic fungi are a large and diverse assemblage of eukaryotes with substantial impacts on natural ecosystems and human endeavours. These taxa often have complex and poorly understood life cycles, lack observable, discriminatory morphological characters, and may not be amenable to in vitro culturing. As a result, species identification is frequently difficult. Molecular (DNA sequence) data have emerged as crucial information for the taxonomic identification of plant pathogenic fungi, with the nuclear ribosomal internal transcribed spacer (ITS) region being the most popular marker. However, international nucleotide sequence databases are accumulating numerous sequences of compromised or low-resolution taxonomic annotations and substandard technical quality, making their use in the molecular identification of plant pathogenic fungi problematic. Here we report on a concerted effort to identify high-quality reference sequences for various plant pathogenic fungi and to re-annotate incorrectly or insufficiently annotated public ITS sequences from these fungal lineages. A third objective was to enrich the sequences with geographical and ecological metadata. The results – a total of 31,954 changes – are incorporated in and made available through the UNITE database for molecular identification of fungi (http://unite.ut.ee), including standalone FASTA files of sequence data for local BLAST searches, use in the next-generation sequencing analysis platforms QIIME and mothur, and related applications. The present initiative is just a beginning to cover the wide spectrum of plant pathogenic fungi, and we invite all researchers with pertinent expertise to join the annotation effort.

Fruiting body and soil rDNA sampling detects complementary assemblage of Agaricomycotina (Basidiomycota, Fungi) in a hemlock‐dominated forest plot in southern Ontario

This is the first study to assess the diversity and community structure of the Agaricomycotina in an ectotrophic forest using above‐ground fruiting body surveys as well as soil rDNA sampling. We recovered 132 molecular operational taxonomic units, or ‘species’, from fruiting bodies and 66 from soil, with little overlap. Fruiting body sampling primarily recovered fungi from the Agaricales, Russulales, Boletales and Cantharellales. Many of these species are ectomycorrhizal and form large fruiting bodies. Soil rDNA sampling recovered fungi from these groups in addition to taxa overlooked during the fruiting body survey from the Atheliales, Trechisporales and Sebacinales. Species from these groups form inconspicuous, resupinate and corticioid fruiting bodies. Soil sampling also detected fungi from the Hysterangiales that form fruiting bodies underground. Generally, fruiting body and soil rDNA samples recover a largely different assemblage of fungi at the species level; however, both methods identify the same dominant fungi at the genus‐order level and ectomycorrhizal fungi as the prevailing type. Richness, abundance, and phylogenetic diversity (PD) identify the Agaricales as the dominant fungal group above‐ and below‐ground; however, we find that molecularly highly divergent lineages may account for a greater proportion of total diversity using the PD measure compared with richness and abundance. Unless an exhaustive inventory is required, the rapidity and versatility of DNA‐based sampling may be sufficient for a first assessment of the dominant taxonomic and ecological groups of fungi in forest soil.

Structure, function, and phylogeny of the mating locus in the Rhizopus oryzae complex.

The Rhizopus oryzae species complex is a group of zygomycete fungi that are common, cosmopolitan saprotrophs. Some strains are used beneficially for production of Asian fermented foods but they can also act as opportunistic human pathogens. Although R. oryzae reportedly has a heterothallic (+/−) mating system, most strains have not been observed to undergo sexual reproduction and the genetic structure of its mating locus has not been characterized. Here we report on the mating behavior and genetic structure of the mating locus for 54 isolates of the R. oryzae complex. All 54 strains have a mating locus similar in overall organization to Phycomyces blakesleeanus and Mucor circinelloides (Mucoromycotina, Zygomycota). In all of these fungi, the minus (−) allele features the SexM high mobility group (HMG) gene flanked by an RNA helicase gene and a TP transporter gene (TPT). Within the R. oryzae complex, the plus (+) mating allele includes an inserted region that codes for a BTB/POZ domain gene and the SexP HMG gene. Phylogenetic analyses of multiple genes, including the mating loci (HMG, TPT, RNA helicase), ITS1-5.8S-ITS2 rDNA, RPB2, and LDH genes, identified two distinct groups of strains. These correspond to previously described sibling species R. oryzae sensu stricto and R. delemar. Within each species, discordant gene phylogenies among multiple loci suggest an outcrossing population structure. The hypothesis of random-mating is also supported by a 50∶50 ratio of plus and minus mating types in both cryptic species. When crossed with tester strains of the opposite mating type, most isolates of R. delemar failed to produce zygospores, while isolates of R. oryzae produced sterile zygospores. In spite of the reluctance of most strains to mate in vitro, the conserved sex locus structure and evidence for outcrossing suggest that a normal sexual cycle occurs in both species.

Factors That Affect Large Subunit Ribosomal DNA Amplicon Sequencing Studies of Fungal Communities: Classification Method, Primer Choice, and Error

Nuclear large subunit ribosomal DNA is widely used in fungal phylogenetics and to an increasing extent also amplicon-based environmental sequencing. The relatively short reads produced by next-generation sequencing, however, makes primer choice and sequence error important variables for obtaining accurate taxonomic classifications. In this simulation study we tested the performance of three classification methods: 1) a similarity-based method (BLAST + Metagenomic Analyzer, MEGAN); 2) a composition-based method (Ribosomal Database Project naïve Bayesian classifier, NBC); and, 3) a phylogeny-based method (Statistical Assignment Package, SAP). We also tested the effects of sequence length, primer choice, and sequence error on classification accuracy and perceived community composition. Using a leave-one-out cross validation approach, results for classifications to the genus rank were as follows: BLAST + MEGAN had the lowest error rate and was particularly robust to sequence error; SAP accuracy was highest when long LSU query sequences were classified; and, NBC runs significantly faster than the other tested methods. All methods performed poorly with the shortest 50–100 bp sequences. Increasing simulated sequence error reduced classification accuracy. Community shifts were detected due to sequence error and primer selection even though there was no change in the underlying community composition. Short read datasets from individual primers, as well as pooled datasets, appear to only approximate the true community composition. We hope this work informs investigators of some of the factors that affect the quality and interpretation of their environmental gene surveys.

Are similarity- or phylogeny-based methods more appropriate for classifying internal transcribed spacer (ITS) metagenomic amplicons?

• The internal transcribed spacer (ITS) of the nuclear ribosomal DNA region is a widely used species marker for plants and fungi. Recent metagenomic studies using next-generation sequencing, however, generate only partial ITS sequences. Here we compare the performance of partial and full-length ITS sequences with several classification methods. • We compiled a full-length ITS data set and created short fragments to simulate the read lengths commonly recovered from current next-generation sequencing platforms. We compared recovery, erroneous recovery, and coverage for the following methods: best BLAST hit classification, MEGAN classification, and automated phylogenetic assignment using the Statistical Assignment Program (SAP). • We found that summarizing results with more inclusive taxonomic ranks increased recovery and reduced erroneous recovery. The similarity-based methods BLAST and MEGAN performed consistently across most fragment lengths. Using a phylogeny-based method, SAP runs with queries 400 bp or longer worked best. Overall, BLAST had the highest recovery rates and MEGAN had the lowest erroneous recovery rates. • A high-throughput ITS classification method should be selected, taking into consideration read length, an acceptable tradeoff between maximizing the total number of classifications and minimizing the number of erroneous classifications, and the computational speed of the assignment method.

Ancient pathogen DNA in archaeological samples detected with a Microbial Detection Array.

Ancient human remains of paleopathological interest typically contain highly degraded DNA in which pathogenic taxa are often minority components, making sequence-based metagenomic characterization costly. Microarrays may hold a potential solution to these challenges, offering a rapid, affordable, and highly informative snapshot of microbial diversity in complex samples without the lengthy analysis and/or high cost associated with high-throughput sequencing. Their versatility is well established for modern clinical specimens, but they have yet to be applied to ancient remains. Here we report bacterial profiles of archaeological and historical human remains using the Lawrence Livermore Microbial Detection Array (LLMDA). The array successfully identified previously-verified bacterial human pathogens, including Vibrio cholerae (cholera) in a 19th century intestinal specimen and Yersinia pestis (“Black Death” plague) in a medieval tooth, which represented only minute fractions (0.03% and 0.08% alignable high-throughput shotgun sequencing reads) of their respective DNA content. This demonstrates that the LLMDA can identify primary and/or co-infecting bacterial pathogens in ancient samples, thereby serving as a rapid and inexpensive paleopathological screening tool to study health across both space and time.

Rapid and accurate taxonomic classification of insect (class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier

Current methods to identify unknown insect (class Insecta) cytochrome c oxidase (COI barcode) sequences often rely on thresholds of distances that can be difficult to define, sequence similarity cut‐offs, or monophyly. Some of the most commonly used metagenomic classification methods do not provide a measure of confidence for the taxonomic assignments they provide. The aim of this study was to use a naïve Bayesian classifier (Wang et al. Applied and Environmental Microbiology, 2007; 73: 5261) to automate taxonomic assignments for large batches of insect COI sequences such as data obtained from high‐throughput environmental sequencing. This method provides rank‐flexible taxonomic assignments with an associated bootstrap support value, and it is faster than the blast‐based methods commonly used in environmental sequence surveys. We have developed and rigorously tested the performance of three different training sets using leave‐one‐out cross‐validation, two field data sets, and targeted testing of Lepidoptera, Diptera and Mantodea sequences obtained from the Barcode of Life Data system. We found that type I error rates, incorrect taxonomic assignments with a high bootstrap support, were already relatively low but could be lowered further by ensuring that all query taxa are actually present in the reference database. Choosing bootstrap support cut‐offs according to query length and summarizing taxonomic assignments to more inclusive ranks can also help to reduce error while retaining the maximum number of assignments. Additionally, we highlight gaps in the taxonomic and geographic representation of insects in public sequence databases that will require further work by taxonomists to improve the quality of assignments generated using any method.