Publications

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Environmental DNA (DNA) is usually defined as genetic material obtained directly from environmental samples, such as soil, water, or ice. Coupled to DNA metabarcoding, DNA is a powerful tool in biodiversity assessments. Results from eDNA approach provided valuable insights to the studies of past and contemporary biodiversity in terrestrial and aquatic environments. However, the state and fate of DNA are still investigated and the knowledge about the form of DNA (i.e., extracellular vs. intracellular) or the DNA degradation under different environmental conditions is limited. Here, we tackle this issue by analyzing foraminiferal sedimentary DNA (DNA) from different size fractions of marine sediments: >500 µm, 500-100 µm, 100-63 µm, and < 63 µm. Surface sediment samples were collected at 15 sampling stations located in the Svalbard archipelago. Sequences of the foraminifera-specific 37f region were generated using Illumina technology. The presented data may be used as a reference for a wide range of DNA-based studies, including biomonitoring and biodiversity assessments across time and space.

Erosion of biodiversity generated by anthropogenic activities has been studied for decades and in many areas at the species level, using taxa monitoring. In contrast, genetic erosion within species has rarely been tracked, and is often studied by inferring past population dynamics from contemporaneous estimators. An alternative to such inferences is the direct examination of past genes, by analysing museum collection specimens. While providing direct access to genetic variation over time, historical DNA is usually not optimally preserved, and it is necessary to apply genotyping methods based on hybridization-capture to unravel past genetic variation. In this study, we apply such a method (i.e., HyRAD), to large time series of two butterfly species in Finland, and present a new bioinformatic pipeline, namely PopHyRAD, that standardizes and optimizes the analysis of HyRAD data at the within-species level. In the localities for which the data retrieved have sufficient power to accurately examine genetic dynamics through time, we show that genetic erosion has increased across the last 100 years, as revealed by signatures of allele extinctions and heterozygosity decreases, despite local variations. In one of the two butterflies (Erebia embla), isolation by distance also increased through time, revealing the effect of greater habitat fragmentation over time.

Developmental genes are often controlled by large regulatory landscapes matching topologically associating domains (TADs). In various contexts, the associated chromatin backbone is modified by specific enhancer-enhancer and enhancer-promoter interactions. We used a TAD flanking the mouse cluster to study how these regulatory architectures are formed and deconstructed once their function achieved. We describe this TAD as a functional unit, with several regulatory sequences acting together to elicit a transcriptional response. With one exception, deletion of these sequences didn't modify the transcriptional outcome, a result at odds with a conventional view of enhancer function. The deletion and inversion of a CTCF site located near these regulatory sequences did not affect transcription of the target gene. Slight modifications were nevertheless observed, in agreement with the loop extrusion model. We discuss these unexpected results considering both conventional and alternative explanations relying on the accumulation of poorly specific factors within the TAD backbone.

Human leucocyte antigen (HLA) alleles and single nucleotide polymorphisms (SNPs) lying in the HLA region are known to be associated with several infectious diseases among which acquired immunodeficiency syndrome, hepatitis B, hepatitis C, tuberculosis, leprosy and malaria are highly prevalent in many human populations worldwide. Distinct approaches such as case-control comparisons, immunogenetic analyses, bioinformatic peptide-binding predictions, ancient DNA and genome-wide association studies (GWAS) have contributed to improving this knowledge during the last decade, although many results still need stronger statistical and/or functional support. The present review updates the information regarding the main HLA allele and SNP associations observed to date for six of the most widespread and some other infectious diseases, and provides a synthetic illustration of these findings on a schematic HLA genomic map. It then discusses these results by stressing the importance of integrating information on HLA population diversity in disease-association studies.

Increasing anthropogenic impact and global change effects on natural ecosystems has prompted the development of less expensive and more efficient bioassessments methodologies. One promising approach is the integration of DNA metabarcoding in environmental monitoring. A critical step in this process is the inference of ecological quality (EQ) status from identified molecular bioindicator signatures that mirror environmental classification based on standard macroinvertebrate surveys. The most promising approaches to infer EQ from biotic indices (BI) are supervised machine learning (SML) and the calculation of indicator values (IndVal). In this study we compared the performance of both approaches using DNA metabarcodes of bacteria and ciliates as bioindicators obtained from 152 samples collected from seven Norwegian salmon farms. Results from standard macroinvertebrate-monitoring of the same samples were used as reference to compare the accuracy of both approaches. First, SML outperformed the IndVal approach to infer EQ from eDNA metabarcodes. The Random Forest (RF) algorithm appeared to be less sensitive to noisy data (a typical feature of massive environmental sequence data sets) and uneven data coverage across EQ classes (a typical feature of environmental compliance monitoring scheme) compared to a widely used method to infer IndVals for the calculation of a BI. Second, bacteria allowed for a more accurate EQ assessment than ciliate eDNA metabarcodes. For the implementation of DNA metabarcoding into routine monitoring programs to assess ecological quality around salmon aquaculture cages, we therefore recommend bacterial DNA metabarcodes in combination with SML to classify EQ categories based on molecular signatures.

Genome skimming has the potential for generating large data sets for DNA barcoding and wider biodiversity genomic studies, particularly via the assembly and annotation of full chloroplast (cpDNA) and nuclear ribosomal DNA (nrDNA) sequences. We compare the success of genome skims of 2051 herbarium specimens from Norway/Polar regions with 4604 freshly collected, silica gel dried specimens mainly from the European Alps and the Carpathians. Overall, we were able to assemble the full chloroplast genome for 67% of the samples and the full nrDNA cluster for 86%. Average insert length, cover and full cpDNA and rDNA assembly were considerably higher for silica gel dried than herbarium-preserved material. However, complete plastid genomes were still assembled for 54% of herbarium samples compared to 70% of silica dried samples. Moreover, there was comparable recovery of coding genes from both tissue sources (121 for silica gel dried and 118 for herbarium material) and only minor differences in assembly success of standard barcodes between silica dried (89% ITS2, 96% matK and rbcL) and herbarium material (87% ITS2, 98% matK and rbcL). The success rate was > 90% for all three markers in 1034 of 1036 genera in 160 families, and only Boraginaceae worked poorly, with 7 genera failing. Our study shows that large-scale genome skims are feasible and work well across most of the land plant families and genera we tested, independently of material type. It is therefore an efficient method for increasing the availability of plant biodiversity genomic data to support a multitude of downstream applications.

Hybrid zones, whereby divergent lineages come into contact and eventually hybridize, can provide insights on the mechanisms involved in population differentiation and reproductive isolation, and ultimately speciation. Suture zones offer the opportunity to compare these processes across multiple species. In this paper we use reduced-complexity genomic data to compare the genetic and phenotypic structure and hybridization patterns of two mimetic butterfly species, Ithomia salapia and Oleria onega (Nymphalidae: Ithomiini), each consisting of a pair of lineages differentiated for their wing colour pattern and that come into contact in the Andean foothills of Peru. Despite similarities in their life history, we highlight major differences, both at the genomic and phenotypic level, between the two species. These differences include the presence of hybrids, variations in wing phenotype, and genomic patterns of introgression and differentiation. In I. salapia, the two lineages appear to hybridize only rarely, whereas in O. onega the hybrids are not only more common, but also genetically and phenotypically more variable. We also detected loci statistically associated with wing colour pattern variation, but in both species these loci were not over-represented among the candidate barrier loci, suggesting that traits other than wing colour pattern may be important for reproductive isolation. Our results contrast with the genomic patterns observed between hybridizing lineages in the mimetic Heliconius butterflies, and call for a broader investigation into the genomics of speciation in Ithomiini - the largest radiation of mimetic butterflies.