Molecular Ecology Resources最新文献

Snake venoms are complex mixtures of toxic proteins that hold significant medical, pharmacological and evolutionary interest. To better understand the genetic diversity underlying snake venoms, we developed VenomCap, a novel exon-capture probe set targeting toxin-coding genes from a wide range of elapid snakes, with a particular focus on the ecologically diverse and medically important subfamily Hydrophiinae. We tested the capture success of VenomCap across 24 species, representing all major elapid lineages. We included snake phylogenomic probes in the VenomCap capture set, allowing us to compare capture performance between venom and phylogenomic loci and to infer elapid phylogenetic relationships. We demonstrated VenomCap's ability to recover exons from ~1500 target markers, representing a total of 24 known venom gene families, which includes the dominant gene families found in elapid venoms. We find that VenomCap's capture results are robust across all elapids sampled, and especially among hydrophiines, with respect to measures of target capture success (target loci matched, sensitivity, specificity and missing data). As a cost-effective and efficient alternative to full genome sequencing, VenomCap can dramatically accelerate the sequencing and analysis of venom gene families. Overall, our tool offers a model for genomic studies on snake venom gene diversity and evolution that can be expanded for comprehensive comparisons across the other families of venomous snakes.

For two decades, DNA barcoding and, more recently, DNA metabarcoding have been used for molecular species identification and estimating biodiversity. Despite their growing use, few studies have systematically evaluated these methods. This study aims to evaluate the efficacy of barcoding methods in identifying species and estimating biodiversity, by assessing their consistency with traditional morphological identification and evaluating how assignment consistency is influenced by taxonomic group, sequence similarity thresholds and geographic distance. We first analysed 951 insect specimens across three taxonomic groups: butterflies, bumblebees and parasitic wasps, using both morphological taxonomy and single-specimen COI DNA barcoding. An additional 25,047 butterfly specimens were identified by COI DNA metabarcoding. Finally, we performed a systematic review of 99 studies to assess average consistency between insect species identity assigned via morphology and COI barcoding and to examine the distribution of research effort. Species assignment consistency was influenced by taxonomic group, sequence similarity thresholds and geographic distance. An average assignment consistency of 49% was found across taxonomic groups, with parasitic wasps displaying lower consistency due to taxonomic impediment. The number of missing matches doubled with a 100% sequence similarity threshold and COI intraspecific variation increased with geographic distance. Metabarcoding results aligned well with morphological biodiversity estimates and a strong positive correlation between sequence reads and species abundance was found. The systematic review revealed an 89% average consistency and also indicated taxonomic and geographic biases in research effort. Together, our findings demonstrate that while problems persist, barcoding approaches offer robust alternatives to traditional taxonomy for biodiversity assessment.

DNA methylation (DNAm) is a mechanism for rapid acclimation to environmental conditions. In natural systems, small effect sizes relative to noise necessitates large sampling efforts to detect differences. Large numbers of individually sequenced libraries are costly. Pooling DNA prior to library preparation may be an efficient way to reduce costs and increase sample size, yet there are to date no recommendations in ecological epigenetics research. We test whether pooled and individual libraries yield comparable DNAm signals in a natural system exposed to different pollution levels by generating whole-epigenome data from two invasive molluscs (Corbicula fluminea, Dreissena polymorpha) collected from polluted and unpolluted localities (Italy). DNA of the same individuals were used for pooled and individual epigenomic libraries and sequenced with equivalent resources per individual. We found that pooling effectively captures similar genome-wide and global methylation signals as individual libraries, highlighting that pooled libraries are representative of the global population signal. However, pooled libraries yielded orders of magnitude more data than individual libraries, which was a consequence of higher coverage. We would therefore recommend aiming for a high initial coverage of individual libraries (15×) in future studies. Consequently, we detected many more differentially methylated regions (DMRs) with the pooled libraries and a significantly lower statistical power for regions from individual libraries. Computationally pooled data from the individual libraries produced fewer DMRs and the overlap with wet-lab pooled DMRs was relatively low. We discuss possible causes for discrepancies, list benefits and drawbacks of pooling, and provide recommendations for future epigenomic studies.

Antarctic krill (Euphausia superba Dana) is a keystone species in the Southern Ocean ecosystem, with ecological and commercial significance. However, its vulnerability to climate change requires an urgent investigation of its adaptive potential to future environmental conditions. Historical museum collections of krill from the early 20th century represent an ideal opportunity to investigate how krill have changed over time due to predation, fishing and climate change. However, there is currently no cost-effective method for implementing population scale collection genomics for krill given its genome size (48 Gbp). Here, we assessed the utility of two inexpensive methods for population genetics using historical krill samples, specifically low-coverage shotgun sequencing (i.e. ‘genome-skimming’) and exome capture. Two full-length transcriptomes were generated and used to identify 166 putative gene targets for exome capture bait design. A total of 20 historical krill samples were sequenced using shotgun and exome capture. Mitochondrial and nuclear ribosomal sequences were assembled from both low-coverage shotgun and off-target of exome capture data demonstrating that endogenous DNA sequences could be assembled from historical collections. Although, mitochondrial and ribosomal sequences are variable across individuals from different populations, phylogenetic analysis does not identify any population structure. We find exome capture provides approximately 4500-fold enrichment of sequencing targeted genes, suggesting this approach can generate the sequencing depth required to call identify a significant number of variants. Unlocking historical collections for genomic analyses using exome capture, will provide valuable insights into past and present biodiversity, resilience and adaptability of krill populations to climate change.

The origin of sociality represents one of the most important evolutionary transitions. Insect sociality evolved in some hemipteran aphids, which can produce soldiers and normal nymphs with distinct morphology and behaviour through parthenogenesis. The lack of genomic data resources has hindered the investigations into molecular mechanisms underlying their social evolution. Herein, we generated the first chromosomal-level genome of a social hemipteran (Pseudoregma bambucicola) with highly specialized soldiers and performed comparative genomic and transcriptomic analyses to elucidate the molecular signatures and regulatory mechanisms of caste differentiation. P. bambucicola has a larger known aphid genome of 582.2 Mb with an N50 length of 11.24 Mb, and about 99.6% of the assembly was anchored to six chromosomes with a scaffold N50 of 98.27 Mb. A total of 14,027 protein-coding genes were predicted and 37.33% of the assembly were identified as repeat sequences. The social evolution is accompanied by a variety of changes in genome organization, including expansion of gene families related to transcription factors, transposable elements, as well as species-specific expansions of certain sugar transporters and UGPases involved in carbohydrate metabolism. We also characterized large candidate gene sets linked to caste differentiation and found evidence of expression regulation and positive selection acting on energy metabolism and muscle structure, explaining the soldier-specific traits including morphological and behavioural specialization, developmental arrest and infertility. Overall, this study offers new insights into the molecular basis of social aphids and the evolution of insect sociality and also provides valuable data resources for further comparative and functional studies.

Fish ear bones, known as otoliths, are often collected in fisheries to assist in management, and are a common sample type in museum and national archives. Beyond their utility for ageing, morphological and trace element analysis, otoliths are a repository of valuable genomic information. Previous work has shown that DNA can be extracted from the trace quantities of tissue remaining on the surface of otoliths, despite the fact that they are often stored dry at room temperature. However, much of this work has used reduced representation sequencing methods in clean lab conditions, to achieve adequate yields of DNA, libraries and ultimately single-nucleotide polymorphisms (SNPs). Here, we pioneer the use of small-scale (spike-in) sequencing to screen contemporary otolith samples prepared in regular molecular biology (in contrast to clean) laboratories for contamination and quality levels, submitting for whole-genome resequencing only samples above a defined endogenous DNA threshold. Despite the typically low quality and quantity of DNA extracted from otoliths, we are able to produce whole-genome libraries and ultimately sets of filtered, unlinked and even putatively adaptive SNPs of ample numbers for downstream uses in population, climate and conservation genomics. By comparing with a set of tissue samples from the same species, we are able to highlight the quality and efficacy of otolith samples from DNA extraction and library preparation, to bioinformatic preprocessing and SNP calling. We provide detailed schematics, protocols and scripts of our approach, such that it can be adopted widely by the community, improving the use of otoliths as a source of valuable genomic data.

Leveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations, we first show that genotype-based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post-Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC-based estimations of s and T for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease-risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. These findings collectively demonstrate that detecting the footprints of natural selection on ancient genomes is crucial for unravelling the history of severe human diseases.

Rapid environmental change poses unprecedented challenges to species persistence. To understand the extent that continued change could have, genomic offset methods have been used to forecast maladaptation of natural populations to future environmental change. However, while their use has become increasingly common, little is known regarding their predictive performance across a wide array of realistic and challenging scenarios. Here, we evaluate the performance of currently available offset methods (gradientForest, the Risk-Of-Non-Adaptedness, redundancy analysis with and without structure correction and LFMM2) using an extensive set of simulated data sets that vary demography, adaptive architecture and the number and spatial patterns of adaptive environments. For each data set, we train models using either all, adaptive or neutral marker sets and evaluate performance using in silico common gardens by correlating known fitness with projected offset. Using over 4,849,600 of such evaluations, we find that (1) method performance is largely due to the degree of local adaptation across the metapopulation (LA), (2) adaptive marker sets provide minimal performance advantages, (3) performance within the species range is variable across gardens and declines when offset models are trained using additional non-adaptive environments and (4) despite (1) performance declines more rapidly in globally novel climates (i.e. a climate without an analogue within the species range) for metapopulations with greater LA than lesser LA. We discuss the implications of these results for management, assisted gene flow and assisted migration.

Environmental DNA (eDNA) preserved in marine sediments is increasingly being used to study past ecosystems. However, little is known about how accurately marine biodiversity is recorded in sediment eDNA archives, especially planktonic taxa. Here, we address this question by comparing eukaryotic diversity in 273 eDNA samples from three water depths and the surface sediments of 24 stations in the Nordic Seas. Analysis of 18S-V9 metabarcoding data reveals distinct eukaryotic assemblages between water and sediment eDNA. Only 40% of Amplicon Sequence Variants (ASVs) detected in water were also found in sediment eDNA. Remarkably, the ASVs shared between water and sediment accounted for 80% of total sequence reads suggesting that a large amount of plankton DNA is transported to the seafloor, predominantly from abundant phytoplankton taxa. However, not all plankton taxa were equally archived on the seafloor. The plankton DNA deposited in the sediments was dominated by diatoms and showed an underrepresentation of certain nano- and picoplankton taxa (Picozoa or Prymnesiophyceae). Our study offers the first insights into the patterns of plankton diversity recorded in sediment in relation to seasonality and spatial variability of environmental conditions in the Nordic Seas. Our results suggest that the genetic composition and structure of the plankton community vary considerably throughout the water column and differ from what accumulates in the sediment. Hence, the interpretation of sedimentary eDNA archives should take into account potential taxonomic and abundance biases when reconstructing past changes in marine biodiversity.

Marine mammals play a fundamental role in the functioning of healthy marine ecosystems and are important indicator species. Studying their biology, distributions, behaviour and health are still technically and logistically demanding for researchers. However, the efforts and commitment have not been in vain, since we are witnessing constant and exponential advancement in the study of these animals, thanks to technological progress in numerous fields. These include miniaturization and performance of biologger tags, which are equipped with sensors for measuring physiological parameters, hydrophones, accelerometers, time-depth records and spatial locations; the use of high throughput ‘Next Generation’ Sequencing to gain genetic information about communities and individual species from nucleic acids in environmental samples at miniscule concentrations; through, to the possibility of monitoring species with autonomous aerial and underwater vehicles. In parallel advances in computing and statistical modelling frameworks support the analysis of increasingly large and complex data sets. In this issue, O'Mahony et al. (2024) draw from at least two of these innovations: (a) the collection of biological material retrieved from large whales' blows using a modified drone and (b) the use of the samples to infer a wide spectrum of genetic information (both nuclear and mitochondrial) about the target animal/population. The methodology is not completely novel, but the study shows an impressive advancement in the amount of data obtained compared to preceding studies using the same approach. In the wake of these promising results, future perspectives are evaluated in relation to alternative sampling methodologies currently in use. It is possible to speculate that, in the next few years, the combination of non-invasive molecular profiling and enhanced drone technology (e.g. assembling increasingly smaller components, thus expanding capacity for autonomous operation) will open up perspectives that were unimaginable at the beginning of this millennium.