Evaluating the impacts of metabarcoding primer selection on DNA characterization of diet in an aerial insectivore, the Purple Martin

DNA metabarcoding is a molecular technique frequently used to characterize diet composition of insectivorous birds. However, results are sensitive to methodological decisions made during sample processing, with primer selection being one of the most critical. The most frequently used DNA metabarcoding primer set for avian insectivores is ZBJ. However, recent studies have found that ZBJ produces significant biases in prey classification that likely influence our understanding of foraging ecology. A new primer set, ANML, has shown promise for characterizing insectivorous bat diets with fewer taxonomic biases than ZBJ, but ANML has not yet been used to study insectivorous birds. Here, we evaluate the ANML primer set for use in metabarcoding of avian insectivore diets through comparison with the more commonly used ZBJ primer set. Fecal samples were collected from both adult and nestling Purple Martins (Progne subis subis) at 2 sites in the USA and 1 site in Canada to maximize variation in diet composition and to determine if primer selection impacts our understanding of diet variation among sites. In total, we detected 71 arthropod prey species, 39 families, and 10 orders. Of these, 40 species were uniquely detected by ANML, whereas only 11 were uniquely detected by ZBJ. We were able to classify 54.8% of exact sequence variants from ANML libraries to species compared to 33.3% from ZBJ libraries. We found that ANML outperformed ZBJ for PCR efficacy, taxonomic coverage, and specificity of classification, but that using both primer sets together produced the most comprehensive characterizations of diet composition. Significant variation in both alpha- and beta-diversity between sites was found using each primer set separately and in combination. To our knowledge, this is the first published metabarcoding study using ANML primers to describe avian diet, and also the first to directly compare results returned by ANML and ZBJ primer sets.

RT qLAMP--Direct Detection of SARS-CoV-2 in Raw Sewage

The project purpose was to examine ability of a loop-mediated isothermal amplification (LAMP) assay to quantify SARS-CoV-2 in raw sewage, directly, using no preliminary sample processing for virus concentration and RNA extraction. An objective was to take advantage of extensive recently published work to facilitate process development, principally primer selection, and to use readily available off the shelf materials with conventional lab procedure and equipment. Well-developed and referenced primers for ORF1a, E, and N-gene targets were selected and applied, using commercially available synthetic RNA standards, and raw sewage from a local wastewater agency serving 650,000. County health department monitoring provided current COVID-19 data. Testing defined performance characteristics for each primer set, with significant differences between them. Specific amplification of SARS-CoV-2 RNA was observed using each of the primer sets, with E-gene and N-gene primers most effective. Positive analysis results from all raw sewage samples corresponded to calculated concentrations of virus in 5-10 μL raw sewage aliquots for 25 μL reactions. Results show that even at low reported case rates e.g. 1-10/100,000, SARS-CoV-2 is present in raw sewage at > 1-5/ μL, permitting direct LAMP-based detection. Use of RT qLAMP will facilitate wastewater-based epidemiology as an important component for COVID-19 control.

Small-sample estimation of the mutational support and the distribution of mutations in the SARS-Cov-2 genome

The problem of estimating unknown features of viral species using a limited collection of observations is of great relevance in computational biology. We consider one such particular problem, concerned with determining the mutational support and distribution of the SARS-Cov-2 viral genome and its open reading frames (ORFs). The mutational support refers to the unknown number of sites that is expected to be eventually mutated in the SARS-Cov-2 genome. It may be used to assess the virulence of the virus or guide primer selection for real-time RT-PCR tests during the early stages of an outbreak. Estimating the unknown distribution of mutations in the genome of different subpopulations while accounting for the unseen may aid in discovering adaptation mechanisms used by the virus to evade the immune system. To estimate the mutational support in the small-sample regime, we use GISAID sequencing data and new state-of-the-art polynomial estimation techniques based on weighted and regularized Chebyshev approximations. For distribution estimation, we adapt the well-known Good-Turing estimator. We also perform a differential analysis of mutations and their sites across different populations. Our analysis reveals several findings: First, the mutational supports exhibit significant differences in the ORF6 and ORF7a regions (older vs younger patients), ORF1b and ORF10 regions (females vs males) and as may be expected, in almost all ORFs (for Asia versus Europe and North America). Second, despite the fact that the N region of SARS-Cov-2 has a predicted 10% mutational support, almost all observed mutations fall outside of the two regions of paired primers recommended for testing by the CDC.Author SummaryWe introduce the new problem of small-sample estimation of the number of mutations and the distribution of mutations in viral and bacterial genomes, and in particular, in the SARS-Cov-2 genome. The approach is of interest due to the fact that it aims to predict which regions in the genome will mutate in the future and with what frequency, given only a very limited number of complete viral sequences. This setting is usually encountered during the early stages of an outbreak when it is critical to assess the potential of the virus to gain mutations advantageous for its spreading. The results may also be used to guide the selection of genomic (primer) regions that are not subject to mutational pressure and can consequently be used as identifiers in the process of testing for the disease. They can also highlight differences in the mutation rates and locations of the SARS-Cov-2 virus affecting diverse subpopulations and therefore potentially suggest the role of certain mutations in evading the immune system. Our approach uses a new class of estimation methods that may find other applications in bioinformatics.

DPSN: standardizing the short names of amplicon-sequencing primers to avoid ambiguity

BackgroundAmplicon sequencing is the most widely used sequencing method to evaluate microbial diversity in virtually all environments. Thus, appropriate and specific primers are needed to amplify amplicon regions in amplicon sequencing. For this purpose, the community currently uses probeBase, which curates rRNA-targeted probes and primers.Main BodyWe found that 63.58% of the primers in probeBase have problematic issues in the short name, full name, and/or position. Furthermore, the current convention for short names causes ambiguity. We here introduce our new Database of Primer Scientific Names (DPSN), which is a manually curated database for the 173 primers in probeBase complete with a new short name convention. Building on the work of probeBase, we provide a more user-friendly and standardized system. The new short primer naming convention has three basic components: 5□ position on the sense strand, version, and direction. An additional character for the name of the taxonomic group is also added in front of the name for convenient use. Furthermore, DPSN contains primers for large subunit as well. In order to separate them from the primers for small subunit, a header character is also recommended.ConclusionAll 173 primers in probeBase were corrected according to this new rule, and are stored in DPSN, which is expected to facilitate accurate primer selection and better standardized communication in this field.Database URLThe DPSN database is available in a user-interactive website at http://dpsn.leidailab.cn/

Genetic variability in a Brazilian apple germplasm collection with low chilling requirements

The apple (Malus domestica Borkh) originally evolved to require temperatures below 7.2 °C for the induction of budding and flowering. In Brazil, breeders have overcome the climate barrier and developed the cultivars Anabela, Julieta, Carícia, and Eva, with low chilling requirements and good yield characteristics. These cultivars are grown in many warmer climate countries in South America, Africa, and the Middle East. The apple germplasm collection that originated these cultivars has several genotypes with pedigrees for a low chilling requirement. Knowledge of the variability and genetic relationships among these genotypes may be useful in the development of superior new cultivars. In this work, we first selected the best ISSR (inter-simple sequence repeat) primers for genetic studies in apple, and then we used the selected primers to evaluate the genetic variability of the apple germplasm collection at the Instituto Agronômico do Paraná. The evaluation of 42 ISSR primers in 10 apple genotypes allowed us to select the best nine primers based on the polymorphic information content (PIC) and resolving power (RP) indexes. The primer selection step was robust since the dendrogram obtained with the nine selected primers was the same as the one obtained using all 26 polymorphic primers. Primer selection using PIC and RP indexes allowed us to save about 60% of time and costs in the genetic variability study. The nine ISSR primers showed high levels of genetic variability in the 60 apple genotypes evaluated. The relevance of the primer selection step is discussed from the perspective of saving time and money in germplasm characterization. The high genetic variability and the genetic relationships among the genotypes are discussed from the perspective of the development of new apple cultivars, mainly aiming for a low chilling requirement that can better adapt to current climatic conditions or those that may arise with global warming.