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Biology subjects

de Lange, M.

Publications and source records attributed to de Lange, M..

3 recordsLinked to original sources

High fat diet induces differential age- and gender-dependent changes in neuronal function linked to redox stress.

The prevalence of neurodegenerative diseases, such as Alzheimers disease and Parkinsons disease, is steadily increasing, posing significant challenges to global healthcare systems. Emerging evidence suggests that dietary habits, particularly consumption of high-fat diets specify which fats, may play a pivotal role in the development and progression of neurodegenerative disorders. Moreover, several studies have shed light on the intricate communication between the gut and the brain, known as the gut-brain axis and its involvement in neurodegenerative processes. This study aims to assess the effects of a high-fat dietary intake on various aspects of neuronal function during aging and following gender separation to help understand the potential contributions of diet to neuronal function. To investigate the effects of a high-fat diet, Drosophila melanogaster was used and exposed to standard normal food diet (NF) and high-fat diet (HF). Adults were grouped at 10 and 45 days of age in male and female flies reared under the same conditions. Multiple assays were conducted, showing differential gender- and HF diet-induced oxidative stress levels as determined by malondialdehyde (MDA) measurements, enhanced caspase-3 expression and reduced climbing activity. Adult lifespan under both dietary conditions was unchanged but odour-associated learning ability was reduced in larvae reared in a HF diet. This is the first study to characterise effects of diet on neuronal phenotypes in an age- and gender-specific manner in a Drosophila model. Our findings suggest a HF diet induces differential forms of neuronal dysfunction with age and sex-specific outcomes, characterised by enhanced oxidative stress and cell death which impacts on neuronal and behavioural functions.

neuroscience↗

Environmental DNA analysis needs local reference data to inform taxonomy-based conservation policy:A case study from Aotearoa / New Zealand

Effective management of biodiversity requires regular surveillance of multiple species. Analysis of environmental DNA by metabarcoding (eDNA) holds promise to achieve this relatively easily. However, taxonomic inquiries into eDNA data need suitable molecular reference data, which are often lacking. We evaluate the impact of this reference data void in a case study of fish diversity in the remote fiords of New Zealand. We compared eDNA-derived species identifications against Baited Remote Underwater Video (BRUV) data collected at the same time and locations as the eDNA data. Furthermore, we cross referenced both eDNA and BRUV data against species lists for the same region obtained from literature surveys and the Ocean Biodiversity Information System (OBIS). From all four data sources, we obtained a total of 116 species records (106 ray-finned fishes, 10 cartilaginous fishes; 59 from literature, 44 from eDNA, 25 from BRUV, 25 from OBIS). Concordance of taxonomies between the data sources dissolved with lowering taxonomic levels, most decisively so for eDNA data. BRUV agreed with local biodiversity information much better and fared better in detecting regional biodiversity dissimilarities. We provide evidence that eDNA metabarcoding will remain a powerful but impaired tool for species-level biodiversity management without locally generated reference data.

ecology↗

Antarctic biodiversity predictions through substrate qualities and environmental DNA

Antarctic conservation science is important to enhance Antarctic policy and to understand alterations of terrestrial Antarctic biodiversity. Antarctic conservation will have limited long term effect in the absence of large-scale biodiversity data, but if such data were available, it is likely to improve environmental protection regimes. To enable Antarctic biodiversity prediction across continental spatial scales through proxy variables, in the absence of baseline surveys, we link Antarctic substrate-derived environmental DNA (eDNA) sequence data from the remote Antarctic Prince Charles Mountains to a selected range of concomitantly collected measurements of substrate properties. We achieve this using a statistical method commonly used in machine learning. We find neutral substrate pH, low conductivity, and some substrate minerals to be important predictors of presence for basidiomycetes, chlorophytes, ciliophorans, nematodes, or tardigrades. Our bootstrapped regression reveals how variations of the identified substrate parameters influence probabilities of detecting eukaryote phyla across vast and remote areas of Antarctica. We believe that our work may improve future taxon distribution modelling and aid targeting logistically challenging biodiversity surveys.

ecology↗