Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/164046

Environmental DNA metabarcoding of rivers: Not all eDNA is everywhere, and not all the time

Macher, J.-N.·Leese, F.
View original source ↗Export metadata ↓

Abstract

Environmental DNA metabarcoding has become a popular tool for the assessment of freshwater biodiversity, but it is largely unclear how sampling time and location influence the assessment of communities. Abiotic factors in rivers can change on small spatial and temporal scale and might greatly influence eDNA metabarcoding results. In this study, we sampled three German rivers at four locations per sampling site: 1. Left river side, surface water 2. Right river side, surface water, 3. Left side, close to the riverbed, 4. Right side, close to the riverbed. For the rivers Ruhr and Mohne, sampling was conducted three times in spring, each sampling one week apart. The Ruhr was again sampled in autumn and the Gillbach was sampled in winter. Sequencing on an Illumina MiSeq with COI primers Bf2/BR2 revealed diverse communities (6493 Operational taxonomic units, OTUs), which largely differed between rivers. Communities changed significantly over time in the Ruhr, but not in the Mohne. Sampling location influenced recovered communities in the Mohne and in the Ruhr in autumn. Our results have important implications for future eDNA studies, which should take into account that not all eDNA in rivers is everywhere and not at all times.

Explore related subjects

molecular biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Macher, J.-N., Leese, F.. 2017-07-15. Environmental DNA metabarcoding of rivers: Not all eDNA is everywhere, and not all the time. https://doi.org/10.1101/164046

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Genomic context tunes sequence-encoded RNA processing

Sequence and genomic position jointly determine RNA processing, but at native loci the two are difficult to separate. Here, we uncoupled them by integrating RNA-processing elements into endogenous transcription units at tens of thousands of mapped positions and different elements at shared positions. Genomic position dominated RNA production levels but differentially influenced RNA processing events. A cryptic polyadenylation site, used natively only upon U1 inhibition, was rarely used ectopically. In contrast, introns partially retained at their native loci remained so across thousands of ectopic positions, with a substantial fraction of transcripts reaching the cytoplasm unspliced. SON depletion reduced splicing of two such introns, preferentially near speckles. Chromatin state, GC content, and speckle proximity tuned this behavior without overriding it, and in opposite directions: speckle proximity improved the splicing of one intron and reduced that of another. Specialized introns are therefore portable modules carrying their own processing information.

molecular biology↗
bioRxiv2026

Single-probe, amplification-free multiplex mRNA PNA-FISH maps bacterial gene expression in intact three-dimensional biofilms

Biofilms are spatially structured communities in which local microenvironments drive physiological differentiation, yet mapping gene expression inside intact three-dimensional biofilms remains challenging. Bacterial spatial transcriptomics methods typically rely on tiled probe sets, sequential hybridization rounds, or signal amplification. Here, we show that a single short, directly labelled peptide nucleic acid (PNA) probe per target is sufficient to quantify bacterial mRNA in situ without amplification. With all probes designed for a common hybridization condition, a transcript panel is resolved in one hybridization on a standard confocal microscope, in unmodified cells while preserving biofilm architecture. Benchmarked against the biphasic cycle of Legionella pneumophila, multiplex mRNA PNA-FISH recovers the expected transcriptional switch and agrees quantitatively with RT-qPCR in planktonic and biofilm populations. In flow-cell biofilms, it shows that sessile populations do not recapitulate the planktonic program and that transcriptional differences follow cellular aggregation state rather than depth.

molecular biology↗
bioRxiv2026

Autophagy activator AA-20 engages CDK5 to promote lipophagy and improve neurodegeneration-associated phenotypes

Lipophagy, the selective degradation of lipid droplets (LDs) by autophagy, has the potential to ameliorate neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's (AD), wherein LD accumulation has been linked to the exacerbation of the proteinopathy component of neurodegeneration. Pharmacological lipophagy inducers remain scarce. We previously identified AA-20 as a novel autophagy activator that acts independently of mTORC1 inhibition, thereby providing a means of stimulating autophagy while avoiding the immunosuppressive side effects associated with direct mTORC1 inhibition. Prior work also showed that AA-20 treatment reduces PolyQ aggregate levels in several C. elegans tissues. Here, we demonstrate that AA-20 enhances LD recruitment to autophagosomes and promotes LD degradation via lysosomal acid lipase activation in murine hippocampal neuronal cells, consistent with lipophagy activation. In human Swedish Amyloid Precursor Protein mutated cerebral organoids, two weeks of AA-20 treatment normalized aberrant lipidomic profiles by selectively reducing various disease-associated lipids, while also correcting the AD-associated electrical hyperexcitability phenotype. Moreover, we identified cyclin-dependent kinase 5 (CDK5), a neuronal kinase linked to AD hyperexcitability and PD pathology, as a target of AA-20. AA-20-induced autophagy and lipid clearance in HT22 cells require CDK5. In a neuronal seeding model of -synuclein pathology, AA-20 reduced pS129--synuclein levels. Collectively, these findings indicate AA-20 can promote lipophagic clearance of LDs and mitigate multiple cellular phenotypes associated with lipidopathy and proteinopathy in neurodegenerative disease models.

molecular biology↗