Biology subjects

Majaneva, S.

Publications and source records attributed to Majaneva, S..

5 recordsLinked to original sources

bioRxiv2024

Enhanced Prediction of Seafloor Ecological State Using 16S Nanopore Sequencing

Anthropogenic stress on benthic habitats, particularly from aquaculture, calls for accurate and efficient monitoring of the macrofauna ecological state. Recent advancements in Oxford Nanopore Technology (ONT) together with environmental DNA offers cost-effective and rapid, on-site monitoring of such ecosystems. Previous studies have demonstrated that Nanopore sequencing provides sufficient precision for predicting ecological state, despite reported challenges with sequencing accuracy. In this study, we aim to predict the seafloor ecological state with both Illumina and Nanopore 16S rRNA gene sequencing data and using a combination of machine learning and feature selection. We analyzed 88 seafloor samples from aquaculture sites located on a north-south gradient along the Norwegian coast. Both sequencing methods were evaluated in combination with various bioinformatic approaches in the context of predicting the normalized EQR index (nEQR, standard ecological index based on macroinvertebrate counting) as a metric of seafloor ecosystem status. Our results show that the predictive performance of Illumina and Nanopore sequencing platforms are comparable, establishing Nanopore as a feasible alternative to illumina sequencing. By employing a stabilized LASSO regression, the feature set (potential taxa) was efficiently optimized from thousands to 40-60 OTUs. The feature selection reduced prediction errors to less than half of what was obtained through full feature modeling. This feature set demonstrated strong predictive accuracy across both sequencing technologies, with a high correlation between observed and predicted nEQR values. The Pearson correlation coefficient of 0.98 for Illumina and 0.95 (mean prediction error: {+/-}0.04) for Nanopore data (mean prediction error: {+/-}0.06). This study demonstrates that continual improvements in Nanopore sequencing accuracy, in combination with optimized feature selection on a broader set of samples, provides a precise and cost-effective monitoring method for marine benthic environments.

ecology↗

bioRxiv2024

A Targeted Reference Database for Improved Analysis of Environmental 16S rRNA Oxford Nanopore Sequencing Data

The Oxford Nanopore Technologies (ONT) sequencing platform is compact and efficient, making it suitable for rapid biodiversity assessments in remote areas. Despite its long reads, ONT has a higher error rate compared to other platforms, necessitating high-quality reference databases for accurate taxonomic assignments. However, the absence of targeted databases for underexplored habitats, such as the seafloor, limits ONTs broader applicability for exploratory analysis. To address this, we propose an approach for building environmentally-targeted databases to improve 16S rRNA gene (16S) analysis using Oxford Nanopore Technologies (ONT), using seafloor sediment samples from the Norwegian coast as an example. We started by using Illumina short-read data to create a database of full-length or near full-length 16S sequences from seafloor samples. Initially, amplicons are mapped to the SILVA database, with matches added to our database. Unmatched amplicons are reconstructed using METASEED and Barrnap methodologies with amplicon and metagenome data. Finally, if the previous strategies did not succeed, we included the short-read sequences in the database. This resulted in AQUAeD-DB, which contains 14 545 16S sequences clustered at 95% identity. Comparative database analysis reveal that AQUAeD-DB provides consistent results for both Illumina and Nanopore read assignments (median correlation coefficient: 0.50), whereas a standard database showed a substantially weaker correlation. These findings also emphasize its potential to recognize both high and low-abundance taxa, which could be key indicators in environmental studies. This work highlights the necessity of targeted databases for environmental analysis, especially for ONT-based studies, and lays foundations for future extension of the database.

bioinformatics↗

bioRxiv2024

Key roles of microbial sulfur and ammonium oxidizers for the coastal seafloor ecological state

Recent evidence suggests that there is a major switch in coastal seafloor microbial ecology already at a mildly deteriorated macrofaunal state. This knowledge is of critical value in the management and conservation of the coastal seafloor. We therefore aimed to determine the relationships between seafloor microbiota and macrofauna on a regional scale. We compared prokaryote, macrofauna, chemical, and geographical data from 1,546 seafloor samples which varied in their exposure to aquaculture activities along the Norwegian and Icelandic coasts. We found that the seafloor samples contained either a sulfur oxidizer network (42.4% of samples, n=656), or an ammonium oxidizer network of microbes (44.0% of samples, n=681). Very few samples contained neither network (9.8% of samples, n=151), or both (3.8% of samples, n=58). Samples with a sulfur oxidizer network had a tenfold higher risk of macrofauna loss (odds ratios, 95% CI: 9.5 to 15.6), while those with an ammonium oxidizer network had a tenfold lower risk (95% CI: 0.068 to 0.11). The sulfur oxidizer network was negatively correlated to distance from Norwegian aquaculture sites (Spearman rho = -0.42, p < 0.01), and was present in all Icelandic samples (n=274). The ammonium oxidizer network was absent from Icelandic samples, and positively correlated to distance from Norwegian aquaculture sites (Spearman rho = 0.67, p < 0.01). Based on 357 high-quality metagenome-assembled genomes (MAGs), we found that the main metabolic process for the ammonium oxidizer network was cobalamin-dependent, while the sulfur oxidizer network was associated with both ammonium retention and sulfur metabolism. In conclusion, our findings highlight the critical roles of sulfur and ammonium oxidizers in mild macrofauna deterioration, which should be included as an essential part of seafloor surveillance.

microbiology↗

bioRxiv2024

Coupling hydrodynamic drifting simulations and seasonal demographics to forecast the occurrence of jellyfish blooms

O_LIAlthough jellyfish are an important component of coastal marine communities, their public perception is often tainted by their proclivity for aggregating in vast numbers, known as jellyfish blooms. Jellyfish blooms occur worldwide and are associated with major economic ramifications, particularly throughout the fisheries, aquaculture, and tourism sectors. C_LIO_LIForecasting jellyfish blooms is critical in order to manage and mitigate their ecological and economic impacts, but the complex life cycles and cryptic life stages exhibited by most jellyfish species largely precludes accurate predictions of their temporal and spatial occurrence. C_LIO_LIHere we introduce a predictive framework, combining state-of-the-art hydrodynamic simulations and periodic population modelling approaches, to forecast spatial and temporal patterns in the formation of jellyfish blooms. While this framework is sufficiently flexible for accommodating various bloom-forming jellyfish species and impacted coastal regions worldwide, we focus on moon jellyfish (Aurelia aurita) populations within the Baltic Sea as an illustrative example. C_LIO_LIWe emphasise how, through the iterative process of forecasting and model validation, this framework can provide valuable insights for resolving key gaps in our understanding of the drivers of bloom events. Indeed, our framework offers an approach for identifying the, to date, unknown locations of polyp-beds; a crucial parameter in enhancing our capacity to accurately predict the occurrence of jellyfish blooms. Accordingly, our framework, represents a key decision-support tool for mitigating the socioeconomic impacts of bloom formation. C_LI

ecology↗

bioRxiv2021

Revision and annotation of DNA barcode records for marine invertebrates: report of the 8th iBOL conference hackathon

The accuracy of the identification of unknown specimens using DNA barcoding and metabarcoding relies on reference libraries containing records with reliable taxonomy and sequence quality. A rampant growth in barcode data led to a stringent need for data curation, especially in taxonomically difficult groups such as marine invertebrates. A major effort in curating marine barcode data deposited in the Barcode of Life Data Systems (BOLD) has been undertaken during the 8th International Barcode of Life Conference (Trondheim, Norway, 2019). For practical reasons, only major taxonomic groups were reviewed and annotated (crustaceans, echinoderms, molluscs, and polychaetes). The congruence of Linnean names with Barcode Index Numbers (BINs) was investigated, and the records deemed uncertain were annotated with four tags: a) MIS-ID (misidentified, mislabeled or contaminated records), b) AMBIG (ambiguous records unresolved with the current data), c) COMPLEX (species occurring in multiple BINs), and d) SHARE (barcodes shared between species). A total of 83,712 specimen records corresponding to 7,576 species were reviewed and 39% of the species were tagged (7% MIS-ID, 17% AMBIG, 14% COMPLEX, and 1% SHARE). High percentages (>50%) of AMBIG tags were recorded in gastropods, whereas COMPLEX tags dominated in crustaceans and polychaetes. This high proportion of tagged species reflects either flaws in the barcoding workflow (e.g., misidentification, cross -contamination) or taxonomic difficulties (e.g., synonyms, undescribed species). Although data curation is crucial for barcode applications, such manual efforts of reviewing large datasets are not sustainable and the implementation of automated solutions to the furthest possible extent is hi ghly desirable.

genetics↗