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The regional origin of a food product commonly affects its value. To this, DNA-based identification of tissue remains could offer fine resolution. For honey, this would allow the usage of not only pollen but all plant tissue, and also that of microbes in the product, for discerning the origin. Here we examined how plant, bacterial and fungal taxa identified by DNA metabarcoding and metagenomics differentiate between honey samples from three neighbouring countries. To establish how the taxonomic contents of honey reflect the country of origin, we used joint species distribution modelling. At the lowest taxonomic level by metabarcoding, with operational taxonomic units, the country of origin explained the majority of variation in the data (70-79%), with plant and fungal gene regions providing the clearest distinction between countries. At the taxonomic level of genera, plants provided the most separation between countries with both metabarcoding and metagenomics. The DNA-based methods distinguish the countries more than the morphological pollen identification and the removal of pollen has only a minor effect on taxonomic recovery by DNA. As we find good resolution among honeys from regions with similar biota, DNA-based methods hold great promise for resolving honey origins among more different regions.

A coordinated observational and modelling campaign targeting biogenic aerosols in the air was performed during spring 2021 at two locations in Northern Europe: Helsinki (Finland) and Siauliai (Lithuania), approximately 500 km from each other in north-south direction. The campaign started on March 1, 2021 in Siauliai (12 March in Helsinki) and continued till mid-May in Siauliai (end of May in Helsinki), thus recording the transition of the atmospheric biogenic aerosols profile from winter to summer. The observations included a variety of samplers working on different principles. The core of the program was based on 2- and 2.4--hourly sampling in Helsinki and Siauliai, respectively, with sticky slides (Hirst 24-h trap in Helsinki, Rapid-E slides in Siauliai). The slides were subsequently processed extracting the DNA from the collected aerosols, which was further sequenced using the 3-rd generation sequencing technology. The core sampling was accompanied with daily and daytime sampling using standard filter collectors. The hourly aerosol concentrations at the Helsinki monitoring site were obtained with a Poleno flow cytometer, which could recognize some of the aerosol types. The sampling campaign was supported by numerical modelling. For every sample, SILAM model was applied to calculate its footprint and to predict anthropogenic and natural aerosol concentrations, at both observation sites. The first results confirmed the feasibility of the DNA collection by the applied techniques: all but one delivered sufficient amount of DNA for the following analysis, in over 40% of the cases sufficient for direct DNA sequencing without the PCR step. A substantial variability of the DNA yield has been noticed, generally not following the diurnal variations of the total-aerosol concentrations, which themselves showed variability not related to daytime. An expected upward trend of the biological material amount towards summer was observed but the day-to-day variability was large. The campaign DNA analysis produced the first high-resolution dataset of bioaerosol composition in the North-European spring. It also highlighted the deficiency of generic DNA databases in applications to atmospheric biota: about 40% of samples were not identified with standard bioinformatic methods.

Publisher Copyright: © 2021 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd. To associate specimens identified by molecular characters to other biological knowledge, we need reference sequences annotated by Linnaean taxonomy. In this study, we (1) report the creation of a comprehensive reference library of DNA barcodes for the arthropods of an entire country (Finland), (2) publish this library, and (3) deliver a new identification tool for insects and spiders, as based on this resource. The reference library contains mtDNA COI barcodes for 11,275 (43%) of 26,437 arthropod species known from Finland, including 10,811 (45%) of 23,956 insect species. To quantify the improvement in identification accuracy enabled by the current reference library, we ran 1000 Finnish insect and spider species through the Barcode of Life Data system (BOLD) identification engine. Of these, 91% were correctly assigned to a unique species when compared to the new reference library alone, 85% were correctly identified when compared to BOLD with the new material included, and 75% with the new material excluded. To capitalize on this resource, we used the new reference material to train a probabilistic taxonomic assignment tool, FinPROTAX, scoring high success. For the full-length barcode region, the accuracy of taxonomic assignments at the level of classes, orders, families, subfamilies, tribes, genera, and species reached 99.9%, 99.9%, 99.8%, 99.7%, 99.4%, 96.8%, and 88.5%, respectively. The FinBOL arthropod reference library and FinPROTAX are available through the Finnish Biodiversity Information Facility (www.laji.fi) at https://laji.fi/en/theme/protax. Overall, the FinBOL investment represents a massive capacity-transfer from the taxonomic community of Finland to all sectors of society.

Ulcerative colitis increases colorectal cancer risk by mechanisms that remain incompletely understood. We approached this question by determining the genetic and epigenetic profiles of colitis-associated colorectal carcinomas (CA-CRC). The findings were compared to Lynch syndrome (LS), a different form of cancer predisposition that shares the importance of immunological factors in tumorigenesis. CA-CRCs (n=27) were investigated for microsatellite instability, CpG island methylator phenotype, and somatic mutations of 999 cancer-relevant genes ("Pan-cancer" panel). A subpanel of "Pan-cancer" design (578 genes) was used for LS colorectal tumors (n=28). Mutational loads and signatures stratified CA-CRCs into three subgroups: hypermutated microsatellite-unstable (group 1, n=1), hypermutated microsatellite-stable (group 2, n=9), and non-hypermutated microsatellite-stable (group 3, n=17). The group 1 tumor was the only one with MLH1 promoter hypermethylation and exhibited the mismatch repair deficiency-associated signatures 21 and 15. Signatures 30 and 32 characterized group 2, whereas no prominent single signature existed in group 3. TP53, the most common mutational target in CA-CRC (16/27, 59%), was similarly affected in groups 2 and 3, but DNA repair genes and Wnt signaling genes were mutated significantly more often in group 2. In LS tumors, the degree of hypermutability exceeded that of the hypermutated CA-CRC groups 1 and 2, and somatic mutational profiles and signatures were different. In conclusion, groups 1 (4%) and 3 (63%) comply with published studies, whereas group 2 (33%) is novel. The existence of molecularly distinct subgroups within CA-CRC may guide clinical management, such as therapy options. This article is protected by copyright. All rights reserved.

Over the last two decades, the use of DNA barcodes has transformed our ability to identify and assess life on our planet. Both strengths and weaknesses of the method have been exemplified through thousands of peer-reviewed scientific articles. Given the novel sequencing approaches, currently capable of generating millions of reads at low cost, we reflect on the questions: What will the future bring for DNA barcoding? Will identification of species using short, standardized fragments of DNA stand the test of time? We present reflected opinions of early career biodiversity researchers in the form of a SWOT analysis and discuss answers to these questions., The Research School in Biosystematics (ForBio) is supported by a grant from the Norwegian Biodiversity Information Centre.

Amplicon sequencing of partial regions of the ribosomal RNA loci (rDNA) is widely used to profile microbial communities. However, the rDNA is dynamic and can exhibit substantial interspecific and intraspecific variation in copy number in prokaryotes and, especially, in microbial eukaryotes. As change in rDNA copy number is a common response to environmental change, rDNA copy number is not necessarily a property of a species. Variation in rDNA copy number, especially the capacity for large intraspecific changes driven by external cues, complicates analyses of rDNA amplicon sequence data. We highlight the need to (i) interpret amplicon sequence data in light of possible interspecific and intraspecific variation, and (ii) examine the potential plasticity in rDNA copy number as an important ecological factor to better understand how microbial communities are structured in heterogeneous environments. peerReviewed

The conservation and management of endangered species requires information on their genetic diversity, relatedness and population structure. The main genetic markers applied for these questions are microsatellites and single nucleotide polymorphisms (SNPs), the latter of which remain the more resource demanding approach in most cases. Here, we compare the performance of two approaches, SNPs obtained by restriction-site-associated DNA sequencing (RADseq) and 16 DNA microsatellite loci, for estimating genetic diversity, relatedness and genetic differentiation of three, small, geographically close wild brown trout (Salmo trutta) populations and a regionally used hatchery strain. The genetic differentiation, quantified as F-ST, was similar when measured using 16 microsatellites and 4,876 SNPs. Based on both marker types, each brown trout population represented a distinct gene pool with a low level of interbreeding. Analysis of SNPs identified half- and full-siblings with a higher probability than the analysis based on microsatellites, and SNPs outperformed microsatellites in estimating individual-level multilocus heterozygosity. Overall, the results indicated that moderately polymorphic microsatellites and SNPs from RADseq agreed on estimates of population genetic structure in moderately diverged, small populations, but RADseq outperformed microsatellites for applications that required individual-level genotype information, such as quantifying relatedness and individual-level heterozygosity. The results can be applied to other small populations with low or moderate levels of genetic diversity.

Phytoplankton is the basis for aquatic food webs and mirrors the water quality. Conventionally, phytoplankton analysis has been done using time consuming and partly subjective microscopic observations, but next generation sequencing (NGS) technologies provide promising potential for rapid automated examination of environmental samples. Because many phytoplankton species have tough cell walls, methods for cell lysis and DNA or RNA isolation need to be efficient to allow unbiased nucleic acid retrieval. Here, we analyzed how two phytoplankton preservation methods, three commercial DNA extraction kits and their improvements, three RNA extraction methods, and two data analysis procedures affected the results of the NGS analysis. A mock community was pooled from phytoplankton species with variation in nucleus size and cell wall hardness. Although the study showed potential for studying Lugol-preserved sample collections, it demonstrated critical challenges in the DNA-based phytoplankton analysis in overall. The 18S rRNA gene sequencing output was highly affected by the variation in the rRNA gene copy numbers per cell, while sample preservation and nucleic acid extraction methods formed another source of variation. At the top, sequence-specific variation in the data quality introduced unexpected bioinformatics bias when the sliding-window method was used for the quality trimming of the Ion Torrent data. While DNA-based analyses did not correlate with biomasses or cell numbers of the mock community, rRNA-based analyses were less affected by different RNA extraction procedures and had better match with the biomasses, dry weight and carbon contents, and are therefore recommended for quantitative phytoplankton analyses. peerReviewed