Biology subjects

Stauffer, F.

Publications and source records attributed to Stauffer, F..

3 recordsLinked to original sources

bioRxiv2025

The interaction between aging and DNA damage influences the accumulation of APP in Alzheimer's disease

DNA damage is a major driver of the aging process, and the progression of Alzheimers disease (AD) worsens the situation. Neurons of the AD brain show significantly elevated levels of DNA damage compared to controls. AD is also associated with altered processing of the amyloid precursor protein (APP), best known for its A{beta} proteolytic peptide fragment. In the current work we find that these three elements - age, DNA damage, and APP - are associated with each other. We present evidence from the mouse brain supporting the hypothesis that DNA damage drives increased levels of APP. Using the TUNEL reaction to track DNA damage, we show that TUNEL staining increases significantly with age (6 months to 24 months, 8 females and 14 males total in this study), in lockstep with intracellular APP. To separate correlation from causality we analyzed tissue from mice genetically deficient in ATM (ataxia-telangiectasia mutated), a protein kinase that promotes DNA damage repair. Compared to 6-month wild type mice, the increased TUNEL signal in neurons of 6-month Atm-/- animals was equivalent to that seen in 24-month wild type. Significantly, the APP signal in the Atm-/- cells was also increased, and this correlation was found in both neuronal and non-neuronal cells. These results suggest that the loss of genomic integrity associated with aging is a cellular stressor that increases the levels of APP and thus increases vulnerability to the pathogenesis of AD. SIGNIFICANCE STATEMENTOur work links the genetics of Alzheimers disease (dominant disease-causing mutations in the APP gene) with DNA damage and aging - two factors known as risk factors in AD. The evidence suggests that the enhanced DNA damage associated with the normal aging process, increases the levels of APP thus potentially contributing to A{beta} production and plaque formation.

neuroscience↗

bioRxiv2020

Unravelling the phylogenomic relationships of the most diverse African palm genus Raphia (Calamoideae, Arecaceae)

Palms are conspicuous floristic elements across the tropics. In continental Africa, even though there are less than 70 documented species, they are omnipresent across the tropical landscape. The genus Raphia has 20 accepted species in Africa and one species endemic to the Neotropics. It is the most economically important genus of African palms with most of its species producing food and construction material. Raphia is divided into five sections based on inflorescence morphology. Nevertheless, the taxonomy of Raphia is problematic with no intra-generic phylogenetic study available. We present a phylogenetic study of the genus using a targeted exon capture approach sequencing of 56 individuals representing 18 out of the 21 species. Our results recovered five well supported clades within the genus. Three sections correspond to those based on inflorescence morphology. R. regalis is strongly supported as sister to all other Raphia species and is placed into a newly described section: Erectae. Overall, morphological based identifications agreed well with our phylogenetic analyses, with 12 species recovered as monophyletic based on our sampling. Species delimitation analyses recovered 17 or 23 species depending on the confidence level used. Species delimitation is especially problematic in the Raphiate and Temulentae sections. In addition, our clustering analysis using SNP data suggested that individual clusters matched geographic distribution. The Neotropical species R. taedigera is supported as a distinct species, rejecting the hypothesis of a recent introduction into South America. Our analyses support the hypothesis that the Raphia individuals from Madagascar are potentially a distinct species different from the widely distributed R. farinifera. In conclusion, our results support the infra generic classification of Raphia based on inflorescence morphology, which is shown to be phylogenetically useful. Classification and species delimitation within sections remains problematic even with our phylogenomic approach. Certain widely distributed species could potentially contain cryptic species. More in-depth studies should be undertaken using morphometrics, increased sampling and more variable markers. Our study provides a robust phylogenomic framework that enables further investigation on the biogeographic history, morphological evolution and other eco-evolutionary aspects of this charismatic, socially and economically important palm genus.

plant biology↗

bioRxiv2019

UDCT: Unsupervised data to content transformation with histogram-matching cycle-consistent generative adversarial networks

The segmentation of images is a common task in a broad range of research fields. To tackle increasingly complex images, artificial intelligence (AI) based approaches have emerged to overcome the shortcomings of traditional feature detection methods. Owing to the fact that most AI research is made publicly accessible and programming the required algorithms is now possible in many popular languages, the use of such approaches is becoming widespread. However, these methods often require data labeled by the researcher to provide a training target for the algorithms to converge to the desired result. This labeling is a limiting factor in many cases and can become prohibitively time consuming. Inspired by Cycle-consistent Generative Adversarial Networks (cycleGAN) ability to perform style transfer, we outline a method whereby a computer generated set of images is used to segment the true images. We benchmark our unsupervised approach against a state-of-the-art supervised cell-counting network on the VGG Cells dataset and show that it is not only competitive but can also precisely locate individual cells. We demonstrate the power of this method by segmenting bright-field images of cell cultures, a live-dead assay of C.Elegans and X-ray-computed tomography of metallic nanowire meshes.

bioengineering↗